Monitoring a well barrier

ABSTRACT

A system for wireline service planning and advising includes a receiver, one or more computing system processors, and a transmitter. The receiver is configured to receive, from a user of the system, an objective parameter for interpreting a state of a well barrier. The one or more computing system processors is in communication with the receiver and configured to generate a plurality of candidate services based on the objective parameter and a model of the well barrier, each candidate service specifying sensor data to be acquired using wireline tools, select at least one wireline service from the wireline candidate services based on a selection logic or input by the user, and generate an execution plan specifying operational parameters of the selected wireline service. The transmitter is in communication with the one or more computing system processors and configured to transmit the execution plan to execute the selected wireline service at a wellsite.

CROSS REFERENCE

This application claims the benefit of U.S. Provisional application No.62/895,755, entitled “AUTONOMOUS WIRELINE OPERATIONS IN OIL AND GASFIELD” filed Sep. 4, 2019 and of U.S. Provisional application No.62/925,980 entitled “AUTONOMOUS WIRELINE OPERATIONS IN OIL AND GASFIELD” filed Oct. 25, 2019, the disclosure of which is herebyincorporated herein by reference.

BACKGROUND

During resource explorations and exploitations such as oil or gasexplorations and exploitations, significant planning is conducted toassess different considerations related to the explorations andexploitations operations, such as considerations that affect productionat the resource site and/or well integrity. These considerations mayinclude estimates of how much resource is available to begin with, typesof equipment and systems needed for site planning, specific equipmentconfiguration parameters that enhance optimal resource extraction,feasibility of extracting the resource given geological data of theresource site, regulatory requirements for safely exploiting theresource, etc. Data associated with the resource or installation canprovide experts with information regarding some of the aforementionedconsiderations.

While capturing data at the resource site with sensors has its ownchallenges, a major problem faced during oil or gas explorations, forexample, is leveraging a plurality of data points from resource sitedata to more accurately characterize parameters of interest. Ofparamount importance is the accuracy of models as this facilitatesappropriate parameterizing of equipment at the resource site. If initialmodels and/or updates to the initial resource models are flawed, it notonly leads to a loss of money, but may also lead to non-productive time.Examples of such parameters of interest may include parameters relatedto well integrity of one or more wellbores.

Moreover, manual processes for acquiring model data and configuringequipment are extremely cumbersome, expensive, and error-prone. Suchmanual processes often lack real-time or pseudo-real-time dynamicequipment configuration based on updates to the resource model.

Additionally, it is desirable during the initial planning phase toaccurately model and test multiple scenarios. This can help determinestability parameters for optimally and safely operating site equipment.

In the context of well integrity, measurements performed at the resourcesite to monitor well integrity include measurements relative to the wellintegrity, in order to determine parameters relative to the casingcorrosion or the material placed in the annulus between the casing andthe wellbore (including). Such parameters may for instance be measuredvia acoustic tools and may comprise casing thickness, acoustic impedanceof the annulus or third interface echo. The objective of well integrityis to monitor a well barrier, for example, the interface between thewell and the formation. The barrier generally includes a casing andcement linking the casing to the formation. However, the barrier betweenthe formation and the well may be degraded due to corrosion of thecasing or degraded quality of the cement, for instance.

It is desirable in the oil and gas industry to efficiently monitor thebarrier (in order to make sure it is not degraded) and act upon thebarrier.

BRIEF SUMMARY

The disclosure relates to methods and systems as claimed, for monitoringa well barrier of a wellbore formed in a geological formation at aresource site and/or performing a well integrity service at the resourcesite. The methods and systems as defined in the claims provide anefficient and flexible manner to monitor the well barrier and/or performa well integrity service.

According to one embodiment of this disclosure, the well barrier of awellbore formation can be monitored by using one or more sensorsdeployed at a resource site to capture sensor data associated with thewellbore. These actions can be autonomously, or semi-autonomously,executed by receiving a wellbore model that includes a plurality ofwellbore model parameters, where each wellbore model parameter has atleast one wellbore model parameter value and associated uncertainty.Using the wellbore model, one or more computer system processors can beused to identify a well integrity service to perform at the wellborewith a downhole tool, where the well integrity service includescapturing sensor data associated with the well barrier. The one or morecomputing system processors generate an execution plan, where theexecution plan includes at least one operation associated withperforming the well integrity service, and controls the operation of oneor more equipment at the resource site. The execution plan can beexecuted at the at the resource site, where it controls the operation ofone or more equipment at the resource site, including the downhole toolcapturing the sensor data associated with the well barrier. The capturedor acquired data can be used to update the wellbore model, including thevalue and associated uncertainty of one or more of the wellbore modelparameters. The updated wellbore model can then be used to determine thestate of the well barrier.

The present disclosure also relates to a system for monitoring a wellbarrier of a wellbore and performing a well integrity service at aresource site. The system includes one or more computing systemprocessors and memory storing instructions that are executable by theone or more computing system processors. The system is configured toreceive the wellbore model, where the wellbore model includes one ormore wellbore model parameters, each parameter having a value andassociated uncertainty. The system generates an execution plan thatincludes one or more actions to perform at least one well integrityservice at the resource site. The well integrity service is based on thewellbore model, and uses one or more sensors deployed in the wellbore toacquire or capture data associated with the well barrier. The sensorscan be deployed on one or more equipment at the resource site, includingat least one downhole tool. The system executes one or more actionsassociated with the execution plan, where the actions are at least inpart executed at the resource site. The one or more actions includecontrol operations of the one or more equipment deployed at the resourcesite. The system updates at least one value and associated uncertaintyof the one or more wellbore model parameters. The system uses theupdated wellbore model to determine the state of the well barrier, whichcan be graphically displayed on one or more end user terminals.

Other aspects and advantages will be apparent from the followingdescription and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the aforementioned embodiments as well asadditional embodiments thereof, reference should be made to the DetailedDescription below, in conjunction with the following drawings in whichlike reference numerals refer to corresponding parts throughout thefigures.

FIG. 1 shows a high-level exemplary wireline process for determining oneor more answer products of a subterranean resource such as an oil or gasreservoir,

FIG. 2A to 2C shows an exemplary cross-sectional view of an oil fieldduring acoustic logging for which the wireline process of FIG. 1 may beimplemented,

FIG. 3 shows an exemplary high-level networked system diagramillustrating a communicative coupling of devices or systems associatedwith the oil field of FIG. 2,

FIG. 4A illustrates an exemplary interaction of a user with the systemof FIG. 3,

FIG. 4B provides further details and more context to the processesdescribed above in conjunction with FIG. 4A,

FIG. 5 illustrates an example of a digital execution plan as per anembodiment of the current disclosure,

FIG. 6 illustrates an example of a wellbore (ie well barrier) modelaccording to an embodiment of the disclosure,

FIG. 7 discloses an embodiment of a method according to the disclosure,in the context of acoustic logging,

FIG. 8A discloses an example of a digital execution plan according to anembodiment of the disclosure,

FIG. 8B discloses an example of logging passes according to a digitalexecution plan.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings and figures. In thefollowing detailed description, numerous specific details are set forthin order to provide a thorough understanding of the invention. However,it will be apparent to one of ordinary skill in the art that theinvention may be practiced without these specific details. In otherinstances, well-known methods, procedures, components, circuits andnetworks have not been described in detail so as not to unnecessarilyobscure aspects of the embodiments.

It will also be understood that, although the terms first, second, etc.,may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are used to distinguish oneelement from another. For example, a first object or step could betermed a second object or step, and, similarly, a second object or stepcould be termed a first object or step, without departing from the scopeof the invention. The first object or step, and the second object orstep, are both objects or steps, respectively, but they are not to beconsidered the same object or step.

The terminology used in the description herein is for the purpose ofdescribing particular embodiments and is not intended to be limiting. Asused in the description of the invention and the appended claims, thesingular forms “a,” “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses any possible combination of one or more of the associatedlisted items. It will be further understood that the terms “includes,”“including,” “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context.

Those with skill in the art will appreciate that while some terms inthis disclosure may refer to absolutes, e.g., all source receivertraces, each of a plurality of objects, etc., the methods and techniquesdisclosed herein may also be performed on fewer than all of a giventhing, e.g., performed on one or more components and/or performed on oneor more source receiver traces. Accordingly, in instances in thedisclosure where an absolute is used, the disclosure may also beinterpreted to be referring to a subset.

The systems, methods, processing procedures, techniques and workflowsdisclosed herein are directed to effective modeling and optimizingprocesses at resource sites such as oil and gas fields. In someembodiments, various apparatuses and systems leverage a plurality ofdata associated with a given resource at a well site (such as oil or gasreservoir and/or well) to determine parameters that optimize operations.This may be accomplished using interconnected devices and systems toobtain data associated with parameters of interest and modeling the wellsite and/or operations based on the obtained data. In some cases,results from the modeling are automatically used to configure equipmentas further discussed below. Additionally, the workflows/flowchartsdescribed in this disclosure, according to the some embodiments,implicate a new processing approach (e.g., hardware, special purposeprocessors, and specially programmed general-purpose processors) becausesuch analyses are too complex and cannot be done by a person in the timeavailable or at all. Thus, the described systems and methods aredirected to tangible implementations or solutions to specifictechnological problems in the oil, gas, and water well industries.

Attention is now directed to methods, techniques, infrastructure, andworkflows for wireline operations in oil and gas fields in accordancewith some embodiments Some operations in the processing procedures,methods, techniques, and workflows disclosed herein may be combinedand/or the order of some operations may be changed. Some operations inthe processing procedures, methods, techniques, and workflows disclosedherein may be combined while the order of some operations may bechanged. Some embodiments include an iterative refinement of one or moremodels via feedback loops executed on an algorithmic basis, such as at acomputing device, and/or through user control mechanisms that makedeterminations regarding whether a given action, template, or model issufficiently accurateS

High-Level Process Overview

FIG. 1 shows a high-level exemplary wireline process 100 according tothe disclosure. The process 100 includes obtaining customer inputs at102, generating candidate services by a service advisor at 104,generating an execution plan for a selected service at 106, executing,for example autonomously, the execution plan at a resource site (e.g.,wellsite, reservoir site, wellbore site, etc.) at 108, conducting dataacquisition, for example autonomously using sensors disposed at thewellsite, at 110, and interpreting the acquired data, for example usingautomatic quality control processes, at 112. According to oneembodiment, the exemplary wireline process of FIG. 1 is executed inconjunction with processes of an oil field such as the oil field of FIG.3 discussed below.

Exemplary Resource Site

As previously noted, the systems and methods presently disclosed may beapplicable to exploring subterranean resources such as oil, natural gas,water, and Salar brines. For the purposes of the foregoing, the systemsand methods would be applied to oil exploration.

FIG. 2A shows a cross-sectional view of an oil field 200 for which thewireline process of FIG. 1 may be implemented. More specifically, FIG.2A illustrates tools, equipment, and/or systems that may be used for oilexploration or exploitation processes at the oil field 200. Variousmeasurement tools capable of sensing one or more parameters such asseismic two-way travel time, density, resistivity, production rate,etc., of a subterranean formation and/or geological formations of theoil field may be employed. As an example, wireline tools may be used toobtain measurement information related to attributes of the casing orformation. The wireline tool may include a sonic or ultrasonictransducer to provide measurements on casing geometry. The casinggeometry information may also be provided by finger caliper sensors thatmay be included on the wireline tool. In some embodiments, varioussensors may be located at various positions along a wellbore at the oilfield 200 to monitor and collect data for executing the wireline processof FIG. 1.

FIG. 2A schematically illustrates an example system for evaluating wellintegrity (ie a well barrier), in particular regarding a composition ofannulus behind casing in a well. In particular, FIG. 2A illustratessurface equipment 202 above a geological formation 200. In the exampleof FIG. 2A, a drilling operation has previously been carried out todrill a wellbore 206. In addition, an annular fill 208 has been used toseal an annulus 210—the space between the wellbore 206 and casing joints212 and collars 214—with cementing operations. In some embodiments, theannular fill 208 may include cement, resin, or any other materialsuitable, for filling the annulus 210. As seen in FIG. 1, several casingjoints 212 (also referred to below as casing 212) represent lengths ofpipe that are coupled together by the casing collars 214 to form acasing string which stabilizes the wellbore 206.

The surface equipment 202 may carry out various well logging operationsto detect conditions of the wellbore 16. The well logging operations maymeasure parameters of the geological formation 200 (e.g., resistivity orporosity) and/or the wellbore 206 (e.g., temperature, pressure, fluidtype, or fluid flowrate). Other measurements may provide acoustic cementevaluation and well integrity data (e.g., casing thickness, apparentacoustic impedance, drilling fluid impedance, etc.) that may be used toverify the cement installation and the zonal isolation of the wellbore206. One or more logging tools 216 conveyed through the wellbore with acable 218 may obtain some of these measurements. In some embodiments,drilling fluid or mud 215 may be present around the logging tool 216 asit is conveyed in the wellbore 206.

The logging tool 216 may be deployed inside the wellbore 206 by thesurface equipment 202, which may include a vehicle 220 and a deployingsystem. Data related to the geological formation 200 or the wellbore 206are gathered by the logging tool 216 may be transmitted to the surface.

FIG. 2A also schematically illustrates a magnified view of a portion ofthe cased wellbore 206. The logging tool 26 may acquire data 226 used toevaluate the integrity of the cased wellbore 206. When the acousticlogging tool 216 provides such measurements to the surface equipment 202(e.g., through the cable 218), the surface equipment 202 may pass themeasurements as acoustic data 226 to a data processing system 228 (e.g.,a cement evaluation system) that includes a processor 230, memory 232,storage 234, and/or a display 236. In other examples, the data 226 maybe processed by a similar data processing system 228 at any othersuitable location as will be explained later. For example, in someembodiments, a portion of data processing may be performed by a dataprocessing system 228 at the resource site, and a portion may beprocessed remotely from the wellsite. This will explained in details inthe next section.

The data processing system 228 may collect the data 226 which may beevaluated to estimate properties associated with the integrity of thewellbore 206, such as a thickness of the casing 212, an apparentacoustic impedance of the annular fill 208, and/or an apparent acousticimpedance of the drilling fluid 215.

Computer facilities (ie data processing systems) such as those discussedin association with FIG. 2A may be positioned at various locations aboutthe oil field 200 and/or at remote locations. The data processing system220 may include one or more terminals (designated for instance 320 inthe following) that may be used to communicate with the onsite tools 216and/or offsite operations, as well as with other surface or downholesensors. The data processing systems 228 may be capable of sendingcommands to the oil field equipment/systems (including the acousticlogging tool and surface equipment), and receiving data therefrom. Thesurface unit may also collect data generated during logging operationsand can produce output data, which may be stored or transmitted forfurther processing.

The data collected by sensors may be used alone or in combination withother data. The data may be collected in one or more databases and/ortransmitted on or offsite. The data may be historical data, real timedata, or combinations thereof. The real time data may be used in realtime, or stored for later use. The data may also be combined withhistorical data or other inputs for further analysis or for modelingpurposes to optimize production processes at the oil field 200. In oneembodiment, the data is stored in separate databases, or combined into asingle database.

In some embodiments, the data 226 from the logging tool 216 may be usedto determine the presence of solid cement in the annular fill 208 hasbeen installed as expected. In some cases, the data 226 may be evaluatedto determine whether the cement of the annular fill 208 has a generallysolid character (e.g., as indicated at numeral 238) and therefore hasproperly set. In other cases, the data 226 may indicate the potentialabsence of cement or that the annular fill 208 has a generally liquid orgas character (e.g., as indicated at numeral 240), which may imply thatthe cement of the annular fill 208 has not properly set. Moreover, insome embodiments, the data 226 may be used to indicate variousparameters relating to the wellbore 206, such as parameters of theannular fill 208, the casing 212, and fluid (i.e., drilling fluid, mud)between the casing 212 and the tool 216. For example, when the loggingtool is an acoustic tool, the data processing system 228 may be used toestimate or output an estimated thickness of the casing 212, an acousticimpedance of the annular fill 18, and/or an acoustic impedance of thefluid.

In the following, a “well barrier” will designate what separate thewellbore from the formation. Such well barrier includes the casing andthe annular fill.

Examples of logging tools 216 include acoustic logging tools that areshown on FIGS. 2B and 2C. Such acoustic logging tools may be, forexample, an UltraSonic Imager (USI™) tool and/or an Isolation Scanner™tool by Schlumberger. The acoustic logging tool 216 a may obtainacoustic data 226 to evaluate properties of the cased wellbore 206. Forinstance, the acoustic logging tool 216 may obtain a pulse echomeasurement that exploits the thickness mode (e.g., in the manner of anultrasonic imaging tool) or may perform a pitch-catch measurement thatexploits the casing flexural mode. The ultrasonic pitch-catch techniquemay be based on exciting and detecting from the casing quasi-Lamb modeswith emphasis on the lowest-order anti-symmetric mode (A0) oftenreferred as the flexural mode. The casing flexural mode also radiateselastic energy into the annulus between casing and formation (or betweena primary casing and a secondary one as it occurs for multiple stringsituations). When the annulus is filled with cement, either a shear waveonly or both shear and compressional waves may be radiated into thecement layer, depending on the mechanical properties of the cement orannulus material.

With this in mind, FIG. 2B provides a general example of the operationof the acoustic logging tool 216 a in the wellbore 206. Specifically, atransducer 252 in the acoustic logging tool 26 may emit acoustic waves254 out toward the casing 212. Reflected waves 256, 258, and 260 maycorrespond to interfaces at the casing 212, the annular fill 208, andthe geological formation 204 or an outer casing, respectively. Thereflected waves 256, 258, and 260 may vary depending on whether theannular fill 208 is of the generally solid character 238 or thegenerally liquid or gas character 240. The reflected waves 256, 258, and260 may be received at the same transducer 252 to be processed forcement evaluation. The acoustic logging tool 216 a may use any suitablenumber of different techniques, including measurements of acousticimpedance from sonic waves, ultrasonic waves and/or flexuralattenuation. When one or more of these measurements of acoustic cementevaluation data are obtained, they may be integrated and/or processed todetermine characteristics of the annular fill 208.

FIG. 2C provides another example embodiment of the acoustic logging tool216 b having an emitter 268 and a pair of receiver transducers 270. Theemitter 268 in the acoustic logging tool 216 a may emit acoustic energy272 out toward the casing 212 resulting in reflected waves 274, 276, and278. In the embodiments shown in FIG. 2C, the emitted energy excites apredominantly zeroth-order asymmetric mode (also referred to as flexuralmode). As in the embodiment described above, the acoustic waves 272propagate via transmission into both sides of the casing wall 212. Thetransmission in the casing annulus depends on the material on the outerside of the casing wall with a different amount of energy leak insidethe annulus. The acoustic logging tool embodiment depicted in FIG. 2Cmay use measurements of acoustic impedance from flexural attenuation.The different distance from the emitter 268 and the two receivertransducers 270 and the energy leak induce different amplitudes on themeasured acoustic pressure.

One or more aspects or embodiments of the present techniques may beapplicable to thickness mode, pulse-echo measurements such as thoseobtained by the tool 216 a in FIG. 2B and flexural mode, pitch-catchmeasurements such as those obtained by the tool 216 b in FIG. 2C as wellas other type of measurements for determining the well integrity, e.g.the state of the well barrier. For instance, acoustic data 226 mayinclude acoustic waveforms or reflected waves from the casing 212, theannular fill 208, the formation 204, and/or any of the interfacesbetween mud and the casing 212, annular fill 208, formation 204. Theacoustic data 226 may also be referred to as the acoustic waveforms ormeasured waveforms. Furthermore, even though only acoustic logging toolshave been described in details and will be used as exemplary embodimentsin the following, the method described below may be applied to otherwell integrity tools, such as electromagnetic tools, caliper, etc.

Part, or all, of the oil field 200 may be on land or water. Also, whilea single wellbore at a single location is depicted, the technologydescribed herein may be utilized with any combination of one or moreresource sites (e.g., multiple oil fields or multiple wellsites), one ormore processing facilities, etc.

While a specific well barrier is depicted, it is appreciated that thewellbores may contain a variety of well barriers, sometimes havingextreme complexity. Each of the logging tools may be used to measureproperties of one the well barriers at the resource site. While eachdata acquisition tool is shown as being in specific locations in FIG.2A, it is appreciated that one or more types of measurement may be takenat one or more locations across one or more wellbores 206 in the oilfields 200 or other locations for comparison and/or analysis. The datacollected from various sources at the oil field 200 may be processedand/or evaluated and/or incorporated into models and/or used as trainingdata as further discussed below.

Now that examples of tools used within the method have been described,the method will be explained in more details.

High-Level Networked System

FIG. 3 shows an exemplary high-level networked system diagramillustrating a communicative coupling of devices or systems associatedwith the oil field 200. The system shown in the figure may include oneor more computing system processors 302 a, 302 b, and 302 c forexecuting one or more of the wireline process of FIG. 1. The one or morecomputing system processors 302 may be electrically coupled to one ormore servers (e.g., computing systems) including memory 306 a, 306 b,and 306 c that may store for example, program data, databases, and otherforms of data. Each server of the one or more servers may also includeone or more communication devices 308 a, 308 b, and 308 c. The set ofservers may provide a cloud computing platform 310. In one embodiment,the set of servers includes different computing devices that aresituated in different locations and may be scalable based on the needsand workflows associated with the oil field 200. The communicationdevices of each server may enable the servers to communicate with eachother through a local or global network such as an Internet network. Insome embodiments, the servers may be arranged as a town 312, which mayprovide a private or local cloud service for users. A town may beadvantageous in remote locations with poor connectivity. Additionally, atown may be beneficial in scenarios with large networks where securitymay be of concern. A town in such large network embodiments canfacilitate implementation of a private network within such largenetworks. The town may interface with other towns or a larger cloudnetwork, which may also communicate over public communication links.Note that cloud computing platform 310 may include a private networkand/or portions of public networks. In some cases, a cloud computingplatform 310 may include remote storage and/or other applicationprocessing capabilities.

The system of FIG. 3 may also include one or more user terminals 314 aand 314 b each including at least a processor to execute programs, amemory (e.g., 316 a and 316 b) for storing data, a communication deviceand one or more user interfaces and devices that enable the user toreceive, view, and transmit information. In one embodiment, the userterminals 314 a and 314 b is a computing system having interfaces anddevices including keyboards, touchscreens, display screens, speakers,microphones, a mouse, styluses, etc. The user terminals 314 may becommunicatively coupled to the one or more servers of the cloudcomputing platform 310. The user terminals 314 may be client terminalsor expert terminals, enabling collaboration between clients and expertsthrough the system of FIG. 3.

The system of FIG. 3 may also include at least one or more oil fields200 having, for example, a set of terminals 320 (ie data processingsystem 28 as described in FIG. 2A), each including at least a processor,a memory, a communication device for communicating with other devicescommunicatively coupled to the cloud computing platform 310. The oilfield 200 may also have one or more sensors (e.g., one or more sensorsdescribed in association with FIG. 2) or sensor interfaces 322 a and 322b communicatively coupled to the set of terminals 320 and/or directlycoupled to the cloud computing platform 310. In some exemplaryembodiments, data collected by the one or more sensors/sensor interfaces322 a and 322 b (such as downhole tool 216) may be processed to generatea one or more models which may be displayed on a user interfaceassociated with the set of terminals 320, and/or displayed on userinterfaces associated with the set of servers of the cloud computingplatform 310, and/or displayed on user interfaces of the user terminals314. In some implementations, the one or more models that can simulate awellbore. Furthermore, various equipment/devices discussed inassociation with the oil field 200 may also be communicatively coupledto the set of terminals 320 and or communicatively coupled directly tothe cloud computing platform 310. The equipment and sensors may alsoinclude one or more communication device(s) that may communicate withthe set of terminals 320 to receive orders/instructions locally and/orremotely from the oil field 200 and also send statuses/updates to otherterminals such as the user terminals 314.

The system of FIG. 3 may also include one or more client servers 324including a processor, memory and communication device. Forcommunication purposes, the client servers 324 may be communicativelycoupled to the cloud computing platform 310, and/or to the userterminals 314 a and 314 b, and/or to the set of terminals 320 at the oilfield 200 and/or to sensors at the oil field, and/or to other equipmentat the oil field.

A processor, as discussed with reference to the system of FIG. 3, mayinclude a microprocessor, microcontroller, processor module orsubsystem, programmable integrated circuit, programmable gate array, oranother control or computing device.

The memory/storage media mentioned above can be implemented as one ormore computer-readable or machine-readable storage media that arenon-transitory. In some embodiments, storage media may be distributedwithin and/or across multiple internal and/or external enclosures of acomputing system and/or additional computing systems. Storage media mayinclude one or more different forms of memory including semiconductormemory devices such as dynamic or static random access memories (DRAMsor SRAMs), erasable and programmable read-only memories (EPROMs),electrically erasable and programmable read-only memories (EEPROMs) andflash memories; magnetic disks such as fixed, floppy and removabledisks; other magnetic media including tape; optical media such ascompact disks (CDs) or digital video disks (DVDs), BluRays or any othertype of optical media; or other types of storage devices.“Non-transitory” computer readable medium refers to the medium itself(i.e., tangible, not a signal) and not data storage persistency (e.g.,RAM vs. ROM).

Note that instructions can be provided on one computer-readable ormachine-readable storage medium, or alternatively, can be provided onmultiple computer-readable or machine-readable storage media distributedin a large system having possibly plural nodes and/or non-transitorystorage means. Such computer-readable or machine-readable storage mediumor media is (are) considered to be part of an article (or article ofmanufacture). The storage medium or media can be located either in acomputer system running the machine-readable instructions, or located ata remote site from which machine-readable instructions can be downloadedover a network for execution.

It is appreciated that the described system of FIG. 3 is an example thatmay have more or fewer components than shown, may combine additionalcomponents, and/or may have a different configuration or arrangement ofthe components. The various components shown may be implemented inhardware, software, or a combination of both, hardware and software,including one or more signal processing and/or application specificintegrated circuits.

Further, the steps in the flowcharts described below may be implementedby running one or more functional modules in information processingapparatus such as general-purpose processors or application specificchips, such as ASICs, FPGAs, PLDs, or other appropriate devicesassociated with the system of FIG. 3. These modules, combinations ofthese modules, and/or their combination with general hardware areincluded within the scope of protection of the disclosure.

In some embodiments, a computing system is provided that includes atleast one processor, at least one memory, and one or more programsstored in the at least one memory, such that the programs compriseinstructions, which when executed by the at least one processor, areconfigured to perform any method disclosed herein.

In some embodiments, a computer readable storage medium is provided,which has stored therein one or more programs, the one or more programsincluding instructions, which when executed by a processor, cause theprocessor to perform any method disclosed herein. In some embodiments, acomputing system is provided that includes at least one processor, atleast one memory, and one or more programs stored in the at least onememory for performing any method disclosed herein. In some embodiments,an information processing apparatus for use in a computing system isprovided for performing any method disclosed herein.

Detailed Process Workflow

FIG. 4A illustrates an exemplary interaction of a user with the systemof FIG. 3. As will be realized shortly, this user interaction, accordingto some embodiments, is based on the wireline process of FIG. 1.

As seen in the figure, Column 140 represents the location of the oilfield 200. Column 142 represents services provided by the cloud-basedcomputing platform 310 while column 144 represents a user or a customerinterface.

At 146, the user/customer inputs information about the oil field 200,such as information relative to one or more wellbores and/or wellbarriers. This input may be transmitted to the cloud-based computingplatform 310. When the user is interested in well integrity, theinformation that are inputted may relate to the wellbore profile(including wellbore geometry or wellbore deviation), completion profile(including casing positions, diameters and thicknesses), the boreholefluid properties such as composition, density, slowness etc., theannulus properties, in particular the material situated in the annulusand recent wellbore operations that happened in the wellbore. Accordingto some implementations, the customer inputs may includeparameters/parameter values derived from historical or real-time dataassociated with the oil field 200. As will be further discussed below,the customer inputs may be used to generate an initial modelcharacterizing a wellbore and/or a well barrier at the oil field 200 inorder to initiate operations and/or simulate operations for actualoperations at the oil field 200.

At 148, a cloud-based processing/computing system generates the initialmodel based on the user inputs, which may be a wellbore model, based onthe information provided by the user. For example, a model may beparameterized based on known information or information from modelssimilar to the identified resource.

At 150, the user may input a parameter of interest as an objectiveparameter. The objective parameter is generally a function of one ormore wellbore parameters. When the user is interested in well integrity,the parameter of interest may be related to the state of the barrier,such as material in the wellbore annulus or casing corrosion, forinstance. In some implementations, the value and associated uncertaintyof the objective parameter may be estimated based on the model. As willbe described below, a well integrity service of interest (ie that willhave an influence on the objective parameter) is identified based on thewellbore model and on the objective parameter. In particular,identifying the at least one well integrity service includes simulatingvariation of the value and/or uncertainty of at least one of thewellbore model parameters according to one or more scenarios, andcomputing a forecasted objective parameter value and/or forecastedobjective parameter uncertainty associated with each scenario 151. Theat least one well integrity service is identified based on theforecasted objective parameter value and/or forecasted uncertaintyassociated with each scenario

The method of FIG. 4A discloses one embodiment of identifying the wellintegrity service. In this embodiment, sensitivity data regarding therespective contributions of the wellbore model parameters to theobjective parameter are determined (by performing a global sensitivityanalysis at 152) and one or more target model parameters are identifiedbased on the sensitivity data. A plurality of candidate services areidentified at 154 based on the target model parameters. Then, the methodincludes simulating the variation of the wellbore parameter valuesand/or uncertainties according to a second subset of scenarios (at 156).Each scenario of the second subset is representative of performance ofone of the candidate service, and the simulation according to saidscenario includes incorporating data representative of the performanceof the available into the wellbore model. The at least one wellintegrity service is then identified based at least on the forecastedobjective parameter values and/or uncertainties associated with thesecond subset of scenarios (158-162). This embodiment is described inmore details below.

At 152, the cloud based processing system performs a global sensitivityanalysis. In one embodiment, the global sensitivity analysis includesperforming one or more simulations/tests based on a plurality ofdifferent values of the plurality of parameters that characterize themodel. For example, the one or more simulations could be performed totest the viability of a process to be executed at the oil field 200,and/or to test safety measures to be implemented at the oil field 200.In any case, the simulations, according to some implementations, canfacilitate pre-site (e.g., pre-well) planning processes/proceduresbefore executing such processes and/or procedures at the oil field 200.In some instances, if a set of parameters associated with the model havecertain degrees of uncertainty values or certain introduced measurementerrors and/or manual errors, the simulations may be adapted by varyingone or more values associated with the plurality of the parameterswithin specific ranges (e.g., range of possible values of a parameterthat lead to optimum simulation results aligned with customer/clientobjectives). In some cases, the simulations may be performed based onthe model. The global sensitivity analysis is performed so that thesensitivity of the objective parameter to one or more other parametersof the model is determined and target model parameters having highcontribution to the objective parameter (in particular objectiveparameter uncertainty are identified). The global sensitivity analysisMore information regarding the global sensitivity analysis are providedseparately.

At 154, the simulation results or sensitivity data from the globalsensitivity analysis 152 may be analyzed to identify and/or select oneor more candidate services and/operations and/or tasks that most closelyalign with the objective parameter inputted by the user at 150, inparticular that contribute in reducing the uncertainty of the objectiveparameter. It is appreciated that a candidate service/a recommendedservice, according to some implementations, includes tasks such asacquiring data from a resource using one or more downhole sensors havinga parametrization, such that the parametrization optionally includes avoltage, a gain, a processing time window, an intensity, and aprocessing filter.

Of a plurality of simulations based on the model from 152, one or moreparameters (e.g., resource parameters) associated with the model whosevariance and/or error ranges contribute to uncertainty (e.g.,uncertainty value) and/or undesirable realization of the objectiveparameter may be identified at 154 and subsequently modified to complywith or minimize the uncertainty and/or a desired/optimum realization ofthe objective parameter. In some embodiments, an optimum realization ofthe objective parameter may comprise minimizing one or more uncertaintyvalues associated with the objective parameter.

At 156, a forecast model estimating the effect of selecting a givencandidate service from the one or more candidate services may beexecuted. This corresponds to simulating variation of wellbore modelparameters in response to a scenario associated with the given candidateservice. In some embodiments, estimating the effect of selecting a givencandidate service may comprise generating and ascribing updated valuesfor one or more parameter of the model and associated certainty due tothe performance of one or more candidate services, computing updatedobjective and uncertainty values for the objective parameter and rankingthe candidate service in particular based on the reduction ofuncertainty values of the objective parameter.

According to some implementations, the simulation and/or the forecastmodels may take into account one or more of accuracy, calibration and/orreliability of one or more sensors/equipment at oil field 200. Indeed,the simulation may be based on one or more equipment modelsrepresentative of the equipments at the resource site, including thedownhole tool for performing the candidate service. The equipment modeltakes into account one or more equipment parameters representative ofone or more of accuracy, calibration and/or reliability of theequipment.

In some embodiments, highly ranked services within the one or morecandidate services may be provided to a user via a user interface.

At 158, one or more recommended services from the candidate servicesgenerated may be presented to an user. According to someimplementations, one or more recommended services may be presented at158 for selection by a user with varying degrees of approximation of therequirements of the objective parameter (in particular reduction ofuncertainty of the objective parameter). In other embodiments, anoptimal recommended service may be automatically generated that bestapproximates/closely approximates the requirements of the objectiveparameter (ie the service that enables the greatest reduction ofuncertainty of the objective parameter).

At 160, one or more recommended services may be communicated to the userand displayed on the user's system (e.g., display device such astablets, phones, phablets, laptops, monitors, etc.). According to someimplementations, the one or more recommended services may be displayedon the user's system (e.g., display device) with additional informationincluding one or more of an indicator of the updated uncertainty of theone or more objective parameters, the price of an effective servicewithin the one or more recommended services, and a description of theeffective service. A multi-factor logic may be used to select and/orrank the recommended services.

At 162, the user may select a recommended service (e.g., the effectiveservice). In some cases, a user's selection of a recommended service maybe communicated to the cloud-based computing platform 310.

In an alternative embodiment, the user identifies himself the service hewould like to perform at the oil field and does not use the ServiceAdvisor as described in operations 150-162. The following operationsbased on the selected effective service may however still be performed.In other embodiments, the service advisor may identify candidateservices from which the user chooses directly (without performingoperation 156) and/or does not perform a global sensitivity analysis forselecting a plurality of candidate services (operations 152, 154) butdirectly runs the forecast models (eg simulations) for all of theavailable services.

At 164, the cloud-based computing platform 310 may generate an executionplan for the selected service and communicate the generated executionplan to the oil field 200 and/or to the user's terminal 314. Theexecution plan includes at least one operation comprised in one or moreoperations associated with performing the at least one identifiedservice. The one or more operations may include a sequence of actionsthat control the operation of equipment at the resource site, where theequipment includes at least the downhole tool used to execute theidentified service. Each action may be associated with at least one of asuccess variable and a failure variable. Each action may further beassociated with preconditions, which will be described in greater detailbelow. The execution plan may also comprise one or more models (such aswellbore model, QC model, equipment models) that will enable to guideand/or refine the sequence. When well integrity is the client's concern,the services that are selected may be wireline acoustic services, aloneor in combination with other services such as electromagnetic servicesand/or caliper services. The data that are acquired in relationship withacoustic services (that will be taken as an example in the wholespecification) may relate to acoustic waveforms obtained in response toan emitted signal as explained in more details in relationship to FIGS.2B & 2C. travel time, intensity, spectra, amplitude, etc.

At 166, one or more systems and/or equipment at the oil field 200 mayexecute the execution plan. It includes controlling operation of one ormore equipments, including the downhole tool, at the resource site toperform the at least one identified service. In some cases, data may beobtained during executing the execution plan by one or more site systemsat the oil field 200. In some embodiments, this data may be captured bysensors or other measuring devices at the oil field 200 in real-time asthe execution plan is being implemented. In some embodiments, sensordata measured during the implementation of the execution plan at the oilfield 200 is directly relayed in real-time or pseudo-real-time to thecloud-based computing/processing platform 310, which may update theinitial model with such information, generate additional forecast modelsbased on the updated initial model, revise the execution plan, andtransmit the revised execution plan to the oil field 200 and/or to theuser's system during or after the implementation of the execution plan.In some embodiments, the revised execution plan may be substituted forthe initial execution plan and subsequently implemented at the oil field200.

In some embodiments, sensor data captured during implementation of theexecution plan and/or revised execution plan at oil field 200 may betransmitted to the user so the user can provide updatedinputs/instructions (e.g., at step 168). The updated instructions, insome embodiments, may include instructions to the cloud computingplatform 310 to update the initial model and re-execute steps 152-158 ofFIG. 4A. The operation 168 is optional.

At 170, the one or more systems associated with the oil field 200 mayperform a quality control process based on the acquired sensor data. Theautomated quality control in an action programmed in the execution planand outputs that is determined as either acceptable or undesired. It maybe performed in relationship with the QC model of the execution plan.The automated quality control is explained in more details below.

The one or more systems associated with the oil field 200 may be localand/or remote to the oil field 200. According to some embodiments,results from the quality control process including measurement data maybe transmitted to the cloud computing platform 310, which may refine orupdate, at 172, the resource model (ie wellbore model) or the revisedexecution plan based on the new data as explained in previous paragraph.

Updating the resource model 172 includes updating the value and/oruncertainty of at least one of the wellbore model parameters, therebyupdating the wellbore model, based on data acquired at the resource siteduring the execution of the execution plan. It may include (before orafter the quality control, as will be explained below), interpreting theacquired data to compute one or more updated wellbore model parametervalues and/or uncertainties as part of the execution plan. When the wellintegrity service is an acoustic service, examples of interpretation aregiven later. When the automated data quality control is performed, theat least one wellbore model parameter may be updated when the automatedquality control outputs an acceptable state. Updating the wellbore modelenables to determine a state of the well barrier.

In some instances, the cloud-based computing platform 310 may storeperformance information on equipment used in implementing the executionplan and/or revised execution plan. This performance information may beused to provide models or update models of the equipment for future use.At 174, a product and/or service result and/or operationstatistics/reports and/or resource production information/reports, etc.,may be presented to the user based on the refined model (e.g., modelgenerated from updating the initial model) and the objective parameter.The product may for instance include in this case a state of the wellbarrier.

The processes outlined in FIG. 4A beneficially allow for error-detectionand correction associated with parameters and/or constraints and/orequipment measurements and/or production processes at the oil field 200.For example, and as discussed elsewhere herein, uncertainties associatedwith parameters and/or constraints can be quantified and/or confirmedusing real-time measurements at the oil field based on initial executionplans prior to scaling production operations at the oil field 200.Further, machine learning and/or deep learning, and/or artificialintelligence tools can be used in the interpretation of the acquireddata and/or refinement/updating of models to more accuratelycharacterize operations and/or structures at the oil field 200 asfurther discussed in conjunction with FIG. 4B. For example, simulationtools that may include machine learning or artificial intelligence maybe leveraged to simultaneously run tens, hundreds, or thousands ofsimulations of production processes based on different model parametersto determine models and parameters that satisfy the requirements (inparticular required uncertainty) of the objective parameter.Additionally, the processes of FIG. 4A may facilitate automatic andrapid execution of tasks such as logging, and other extraction andinformation analysis processes at the oil field 200.

FIG. 4B provides further details and more context to the processesdescribed above in conjunction with FIG. 4A. In an embodiment, system400 includes three main blocks, components, or modules including awireline planning module 410, an autonomous execution module 430, and acollaboration, validation, and learning module 450. These modules arefor exemplary purposes only, as system 400 may be divided orcharacterized in other ways, and its modules may vary based on differentdomains (here, well integrity), applications, and contexts. Each of themodules may be implemented by one or more computing system processorsand based on interaction with other elements of the system of FIG. 3.For instance, in the wireline planning module, one or more computingsystem processors can communicate with a user terminal of one or moreuser terminals 314 and/or with a client server 324. In the autonomousexecution module, the one or more computing system processors handlesoperation in coordination with the resource site terminal 320 andequipment and sensors 216 a therein, and possibly with the end userterminals (including the end user terminals of the client and of theexpert). In the collaboration, validation and learning module, the oneor more computing system processors interacts with end user terminals314 and possibly client server 324.

Wireline Planning and Service Advisor

Wireline planning module 410 facilitates job planning and may remain alive workspace throughout the life of an operation or a sequence ofoperations at the oil field 200. In an embodiment, wireline planningmodule 410 includes a service advisor that allows a client (or customer)to assess the value of one or more services against the client'sobjectives. In some cases, with the service advisor includes quicklyobtaining sensitivity analysis results, evaluating impact of anoperation at the oil field 200, and reducing uncertainty of a predictionregarding a resource and/or an operation at the oil field 200 (inparticular the client objectives). In an embodiment, the service advisorin wireline planning module 410 conducts computations in a backend andprovides a user interface in the frontend to a user with desiredoutputs, objectives (e.g., values and/or uncertainties of parameters,derisking elements, etc.), and/or other end products/services.

For example, the wireline planning module 410 receives or generates amodel of a resource of interest. When the client seeks to assess thewell integrity, the model may include a model of a wellbore and/or wellbarrier, and/or a model of equipment that may be used in the wellbore aspart of one or more services. In some cases, the model/resource model isa model estimated based on other models with attributes similar to thoseof the oil field 200. For example, the model may be estimated from anearby wellbore similar to the wellbore of the oil field 200 (ie withsimilar casing and/or cement, etc.). In some instances, the model may beinputted into the wireline module 410 by the user via a user interface.In an embodiment, a client may provide a customer model (e.g.,sub-surface or reservoir model stored in the memory of the client server324) for a service advisor to use in derisking evaluations. In oneembodiment, a model may be generated based on one or more inputs and mayrepresent a current state of a resource at the oil field 200 and mayinclude various information such as model parameter values. In anembodiment, a model represents the current state of a wellbore includinga well barrier and may include various information as needed.

Alternatively or additionally, a client may share with the serviceadvisor certain contextual information (e.g., information about a wellknown to the client, such as wellbore location, wellbore parameters andlogs (including, if available, wellbore profile detailing the pipesettings and borehole geometry and specifications includingrestrictions; wellbore deviation survey, location, state or propertiesof the barriers, the borehole fluid properties, well operations). Amodel may be based on the information provided by the client via a userterminal through an interface or retrieved from a client server 324 andbased on data that is known from other sources of information.

A model may simulate inputs, outputs, results, impacts of differentsensor technologies. The models are used by the service provider todetermine outputs according to client objectives such as whether and howthe well integrity is preserved, how much is the uncertainty level, andhow to reduce the uncertainty level. Therefore, the service providerimproves the client's experience by addressing client objectives withoutoverburdening the client with detailed and complex backend information.In some embodiments, the client may have access to the backend results,for instance upon request.

Based on the model simulating the resource of interest (e.g., wellboreor formation), and one or more objective parameters of the resource setby the client, the client receives results of global sensitivityanalysis (GSA) automatically (e.g., via cloud computing on a cloudserver). Sources of uncertainty that may affect the model include, forexample, lack of information for all or a portion of the wellbore,characteristics of tools capturing the measurement, errors involved inthe interpretation of captured data, errors in computational methods,uncertainties due to assumptions made, and uncertainty due to theresolution of data acquired while drilling or logging. As a result,these uncertainties may propagate into forecasts made. GSA may quantifythe uncertainty in input parameters and provide a possible range forwhich a forecast lies. Sensitivity analysis may describe how theuncertainties (e.g., uncertainty values) in the output of a model orsystem (here, one or more objective parameters) can be apportioned todifferent sources of uncertainties (e.g., uncertainty values) in its(e.g., parameters) comprised in the model. In some instance, sensitivityanalysis may or identify the contribution of different parameters in themodel to the uncertainty of the objective parameters. For example, thesensitivity analysis may determine a degree to which one or moreuncertainty values of one or more parameters of the model contribute toan uncertainty value of the objective parameter. Uncertainty analysismay make a technical contribution to decision-making through thequantification of uncertainties in variables. For instance, uncertaintyanalysis may determine the reliability of model predictions, accountingfor various sources of uncertainty in model input and design.

The service advisor may conduct sensitivity analysis relative to theobjective parameters in order to determine which parameters of the modelhave the highest contribution of the uncertainty to the objectiveparameter. For example, global sensitivity analysis may include varyingsome or all parameters of the model across ranges of their respectiveuncertainties and between constraints that have been provided in themodel. Simulations may be performed to obtain values for the objectiveparameter based on the different possible values for the modelparameters. The results of the simulations can be analyzed and ranked toidentify which parameters of the models are leading to the mostsignificant variations in the uncertainty of the objective parameter.Through multiple simulations which may be run in parallel, thecloud-based computing platform 310 may determine which parameters havehighest level of contributions to variation of the one or more objectiveparameters (i.e., values of the one or more uncertainties of theobjective parameters) and also determine a magnitude of suchcontributions.

As discussed above, simulation data may be analyzed to identify whatmodel parameters are causing the highest uncertainties or whatparameters have the biggest impact to changes in uncertainty values ofthe objective parameter. As an example, if a set of parameters has anuncertainty range from x % to y %, parallel processing may be used tosimulate scenarios for the range x % to y %. Based on the results,parameters associated with a relatively high amount of uncertainty maybe identified and ranked higher (compared to other parameters that causerelatively less uncertainty) as parameters for which data measurementscan remove the most uncertainties. Data acquisition to stabilize suchobjective parameters may be identified as a priority in service planningto reduce the overall output uncertainty.

An example of global sensitivity analysis including modelling ofdifferent scenario is detailed below.

-   -   For individual factors (ie model parameters) of a plurality of        factors (any number of factors may be included):        -   a. Conduct a single-factor sensitivity test based on the            factor that has a certain range of values.            -   i. Estimate a base case (50%), ie likely value of the                factor in view of model constraint (for instance average                value of possible range)            -   ii. Estimate 10% and 90% extremes, ie possible extreme                value of the factor in view of the model        -   b. Vary the factor with others kept at base value.        -   c. Calculate the objective parameters for each scenario.    -   Determine which factors the analysis is sensitive to relative to        other factors, ie to which model parameters at least one of the        objective parameters is particularly sensitive to, ie the        factors that have the highest contributions in the uncertainty        values of the objective parameters. This might be done by        variance analysis, ie Sobol method. More details on such        determination of sensitivity index are disclosed in U.S. Pat.        No. 10,203,428, herein incorporated by reference.

In addition or in the alternative, the simulation model may includeequipment modeling in addition to wellbore modeling. This may be thecase for simulating scenario corresponding to sensor data acquisitionaccording to one or more scenario. This may be performed after theparameters having the highest contribution to the objective parameterare identified and some candidate service that would help the user tofulfill its objective are selected.

For example, a model of sensor response may be included within thesimulation model. The combination of modeling wellbore models withexpected behavior of equipment provides for a better estimation of theresult of performing a certain service. The performance of the equipmenton reducing the uncertainty of an objective parameter, performing aspecified service, of measuring a sensed value may be recorded and addedto a repository of models stored, for example; on the cloud. When theservice advisor later performs the simulation, it may retrieve thesemodels to incorporate expected behavior of equipment in the simulatedmodel. The service advisor may leverage cloud based storage to accessand store equipment models not previously available for simulations. Insome embodiments, the equipment behavior is relatively independent ofthe wellbore model and the repository equipment models may be reasonablyaccurate regardless of where they are deployed. In some embodiments, theequipment behavior is dependent on the well model (eccentricity, numberof casings, etc.) and the service advisor may retrieve equipment modelsassociated with similar conditions as the conditions of the wellboremodel. Thus, the service advisor allows for the realization of newsimulations that take into account not only the parameters of thewellbore model but also the modeled response of equipment used inservices to provide improved and previously unavailable results.

In some embodiments, simulations of wellbore/tools models are conductedvia cloud computing and/or AI technologies, and simulation results canbe quickly incorporated into a model to evolve and improve its qualitysuch as accuracy. Cloud computing and AI technologies may enable accessto greater relevant contextual and/or historical data and may allow forbetter integration of both the service advisor and the client'sknowledge bases. Further, information may be fed back to an end-userquickly with little to no delay in time, thus allowing quicker updatesand decision making by the end-user. Furthermore, cloud computing mayavoid the need for complex computation capabilities of wellsiteequipment which improves processing and simulation capabilities of theoverall system (e.g., thousands of simulations may be conducted andinterpreted each second). The service advisor also uses objectiveparameters input by the client as an objective in order to conduct theanalysis. The objective parameters may be set by the client in aninterface on an end user terminal and thereafter sent to the cloud basedprocessing on the set of servers.

A model (e.g., a customer model or a proxy model), when focused on wellintegrity, includes a well barrier model comprising information aboutthe configuration of the wellbore (well geometry), composition ofborehole fluid, recent wellbore operations, wellbore deviation and stateof the well barrier (ie casing and/or cement) if available. Theobjective parameter may be a state of a barrier (the state of thebarrier including an indicator of the casing corrosion and/or of amaterial within the annulus) and the initial model may have severalbarriers in several locations, each barrier having a different state, iea state that meets accepted barrier criteria, a degraded state, or afailed state that does not meet the accepted barrier criteria.

The service advisor may use objective parameters inputted by theclient/user as an objective in order to conduct an analysis. Theobjective parameters may be set by the client in a user interface of auser terminal and thereafter sent to the cloud-based computing platform310 for processing by one or more servers. In the context of wellintegrity, an objective may be to limit uncertainties regarding thestate of well barriers in one or more wellbores (with the help ofbarrier monitoring sensors that monitor if the barrier meets acceptancecriteria and, if not, the level of degradation of said well barrier)and/or, if a barrier does not meet the accepted barrier criteria, toimprove the state of said barrier (which may be reached via theperformance of one or more services for repairing the well barrier).

The service advisor may identify one or more candidate services with alikelihood of reducing the uncertainty on the objective parameter, orotherwise improving (ie minimizing or maximizing) the objectiveparameter value. The identified candidate services may be communicatedto the user, for example by transmitting and displaying the candidateservices on the user terminal (e.g., display device) of the user. Thecandidate services are identified based on the knowledge of the factorshaving the highest contribution to the uncertainty values of theobjective parameters. In particular the candidate services are theservices that are mapped as reducing the uncertainty value of one ormore of said factors.

Candidate services may include different technologies such as sensors toenable acquisition of data associated with a resource (e.g., wellbore ina particular downhole) at the oil field 200. The services that may beselected as candidate services may be services that acquire dataassociated with parameters of the model (e.g., measurement parameter)that have a high contribution on the uncertainty of the objectiveparameters.

In an embodiment, system 400 may automatically generate a set ofscenarios (e.g., each scenario may run candidate/recommended services).Optionally each candidate service may be run with differentparametrization or operational elements, and/or different values of thedata being simulated with uncertainty fields, and may run simulations ofmost or all scenarios in order to estimate a forecast model associatedwith each scenario having an updated parameter (including the objectiveparameter) and uncertainties (e.g., uncertainty values) taking intoaccount the simulated data in each scenario. In some embodiments, thescenarios may include equipment models such as the expected response ofthe equipment to the environment of the candidate service. The scenariosmay be simulated to account for uncertainty associated with the acquireddata.

In one embodiment, scenarios may be simulated to determine faultyequipment calibration (which may include drift in time), or reliability(which may include influence of other environmental parameters) of thedata acquired that generate uncertainty in the model parameter refinedthanks to data acquisition. A large number (e.g., tens, hundreds, oreven thousands) of simulations may be conducted with varying scenariosin the backend (e.g., within the cloud computing platform 310) withoutbeing visible to a client/user, and the simulations may be fullyautomated based on candidate services. The simulations may becontainerized and executed in parallel, for example using a cloud-basedengine or software as a service platform. In this way, significantcomputing power can be made available to conduct multiple simulations inparallel and in a short time frame. AI-based algorithms and/or machinelearning algorithms and/or deep learning algorithms may be used toimprove efficiency and effectiveness of simulations. The simulation mayvalidate the radius of an investigation associated with the oil field200, resolve quantification of resources at the oil field 200, estimatecapacity of a resource relative to a visualization (e.g., image) of theresource, derisk at the oil field 200, etc. For instance, system 400 mayautomatically rank scenarios based on their comparative performancesover the one or more objective parameters and/or propose a technologysolution, e.g., by selecting certain sensors, acquisition strategy,parametrization, etc. to be executed at the oil field 200.

Based on the forecast models (ie simulations) and the objectiveparameter, one or more effective services may be selected. The one ormore effective services may be chosen automatically or selected by theuser.

In an embodiment, the uncertainty and/or value of one or more objectiveparameters may be determined for each forecast model (ie simulation),with one or more effective services selected using an indicator of theuncertainty and/or value. When one forecast model per candidate servicesis estimated, the indicator may be the uncertainty and/or value of theobjective parameter itself. When several forecast models per candidateservices are estimated, the indicator may be for instance the average ofthe uncertainties or values for each of the forecast models relative tothe candidate service. In another embodiment, when the uncertainty is ofinterest for the user, the highest uncertainty of the forecast modelsrelative to the candidate services will be computed. In someembodiments, estimating one or more forecast models may be based atleast in part on one or more of an accuracy, calibration, and/orreliability of a sensor at the oil field 200, and uncertainty of one ormore updated parameters of the model.

In an embodiment, an effective service may be selected directly by thecloud computing platform 310 based on for example, the indicator ofuncertainty. In another embodiment, one or more candidate services maybe displayed to the user on a user terminal 314. The candidate servicesmay be displayed with additional information such as the above-mentionedindicator, the costs associated with the service, or othercharacteristics and/or descriptions of the service. When several modelsrepresenting several configurations are selected to represent aresource, additional information such as the indicator of uncertainty ofone or more objective parameters for other resource configurations mayalso be provided/displayed as additional information. The user maytherefore select one or more candidate services based on the displayedinformation.

For instance, the candidate services may be chosen in order to take intoaccount several parameters, ie on top of the expressed objectives of theclient, the suitability, cost, effectiveness and efficiency aspects of atechnology in delivering the objective in a specific environment, whichis generally obtained via the tool model for each candidate service andmay as well include the best practices in managing specific scenariossuch as borehole fluid evaluation, pipe thickness, pipe diameter, etc.,as well as the costs of the services. The candidate services may beranked using a multi-factor logic.

In an embodiment, for a given set of objectives (or objectiveparameters), and a given set of pre-populated information (or initialmodel), a service advisor may predict the amount of uncertainty thateach parameter contributes to the objective parameter and/or select aservice that will better reduce the uncertainty of the objectiveparameter. A user/customer may then design/select wireline acquisitionservices to reduce uncertainties based on the operations describedabove.

In another embodiment, for a given set of objectives (or objectiveparameters), and a given set of pre-populated information (or initialmodel), a service advisor may predict the highest/lowest value of theobjective parameter with associated uncertainties and/or select aservice that will better refine the value of the objective parameter asper user's requirements. A user/customer may then design/select wirelineacquisition services to optimize the value of the objective parameterbased on the operations described above.

As mentioned above in relationship with FIG. 4A, the Service Advisor ascomprises a first simulation operation, ie Global Sensitivity Analysis,to identify candidate services (as services having the highestcontribution on an objective parameter value and objective parameteruncertainty value) and then, a second operation for estimating aforecast model (or simulating) one or more scenarios associated with thecandidate services, to identify which service(s) among the candidateservices will enable to have the highest impact on the objectiveparameter.

The first and second operations described above are not always performedin combination and the Service Advisor may, in an alternativeembodiment, perform the first operation (respectively the secondoperation) without the second operation (respectively without the firstoperation). For instance, the Service Advisor may perform a globalsensitivity analysis to determine candidate services that will have animpact on the objective parameter. The recommended services are then thecandidate services user can choose between the candidate servicesaccording to its own knowledge. Alternatively, the Service Advisor caninclude the second operation without the first operation. In this case,the simulations may be run for each available service, which mightnecessitate more time and/or computing power to compute the recommendedservices.

In an alternative embodiment, the user identifies himself the service hewould like to perform at the oil field and does not use the ServiceAdvisor. The following operations based on the selected effectiveservice may however still be performed.

Once the effective service is selected, a digital execution plan (DEP)(or simply, execution plan) may be built/generated and may comprise acomprehensive plan of operations/tasks that are necessary at the oilfield 200 to successfully run/execute the selected effective service. Inone embodiment, the execution plan comprises one or more tasks executedto complete a selected service. The digital execution plan may begenerally built/developed/created on a set of servers associated withthe cloud computing platform 310 with the effective service as an input.In some embodiments, the digital execution plan may be transmitted usinga wired and/or a wireless communication link to the oil field 200.

Digital Execution Plan

The section below described in more details how the digital executionplan is built. In some embodiments, an automated planner develops a planfor execution by a controller to run the effective service. The plan ofthe present disclosure builds greater independence and flexibility intothe plan than prior planning systems.

The plan according to the present disclosure defines, for events in theplan, a set of preceding events, a set of following, or successor,events and at least one condition to detect the event having occurred orto authorise an action to be begun. Time is not a determining factor inthe plan of the disclosure and the plan is executed based upon eventinterdependencies and detected conditions without defining event starttimes, end times or durations in a prescriptive manner. As will becomeapparent from the following description, the plan executed in accordancewith the method of the present disclosure is time independent and cantherefore be executed based purely upon logical constraints and bothplanned and executed incorporating flexibility of time and resourcesavailable to carry out the actions in question.

Turning to the simple plan of the illustrated example, beginning in FIG.5, the plan starts with event #1, which is a event for lowering a toolinto the borehole until an identified target depth. It has successors 2and 3, meaning that events numbered 2 and 3 (all event numbers areillustrated in the Figures in parentheses) are events following event #1and since it is the first event it has no predecessors. For the event #1there is also identified an action number, in this case action #1;denoted on the figures as A1, and an action name, in this caseLowerTool. Several actions may be associated to an event. A parametercan also be set for the action and in this case, the parameter is set asSpeed High, indicating that the controller should aim to achieve a highspeed for lowering the cable. Further, preconditions can be set for theexecution of an event and, in this instance, the preconditions are,firstly, that the tool is mechanically and electrically connected to thecable, defined by the Authorising Precondition C1 ‘connected’. This canbe detected, for example, by pressure/electrical sensors in the logginghead, by sensing tension in the cable, etc. A further precondition C2 isthat the system is not currently being moved, defined as ‘notmoving’,which can be detected by an output from the winch motor, torque sensors,or other suitable means.

Event #1 therefore starts the lowering process and so in response toEvent #1 the controller begins a lowering procedure. The example shownis simplified for ease of reference and conciseness, but in a practicalimplementation, further steps may be present in the plan.

An example of a failure condition C3 may be defined by a parameter whichmust stay true for the action entailed by the event to validly continue.This can be defined as a failed condition—i.e. a condition which candirectly indicate that the event has failed, and potentially that theoverall plan has failed. As an example, Event #1 can have a failedcondition set as ‘notmoving’ while the target depth is not reached.Similarly, other failed conditions may be defined in the plan. As aconsequence, a troubleshooting event (Event #3) is started with anaction A3 and that will not be described here.

A success condition is a physical situation can cause the controller toknow that the lowering operation is complete is that the target depthhas been reached (condition C4). This might be measured for instance bymeasuring the length of the unwound cable. When the success condition C4for Event #1 is obtained, the plan allows for detection of this in Event#2 including action A2 ie stopping. No time constraint is placed onevent #2, but the conditions are that for it to occur, Event #1 musthave occurred and target depth must be reached (condition C4).

Even #4 may then be triggered when event #2 has succeeded, whichcondition may be checked by monitoring the movement of the winch at thesurface (ie winch motor torque is zero for instance—condition C5). Event#4 includes action A4, A4′, A4″ ie setting a parametrization (in thiscase 3 parameters corresponding to the three actions) of the toolsensor(s) as per DEP. A parametrization of the tool may be customized asa function of wellbore/formation physics model related to physicalproperties of a wellbore. For instance, in the well integrity domain,the parametrization of the tool sensors (e.g., firing voltage, windowsettings, filters) will be optimized based on some parameters of thewell (e.g., resonance frequency range, pipe dimensions, mud impedanceand velocity). The success condition of this event is that the toolparametrization for each parameter is the same as the expectedparametrization as per the DEP (condition C6).

Event #5 includes triggering the measurement with the tool sensor(s)that are correctly parametrized. For instance, when the sensor shallmeasure a signal (in the case of an acoustic logging tool, an acousticsignal but in case of other tools any other signal such as current,voltage, particle count, etc.), the condition of success of such eventis the reception of a current that has a non-zero value or a valuehigher than a certain threshold (condition C7). If no signal has beenreceived (condition C8), the event #4 has failed and the event #5 is forinstance restarted (at least once—when event #5 has failed a certainnumber of times other actions might be triggered that are not describedhere for the sake of simplicity).

If a signal has been detected, the plan moves to Event #6 that mayinclude one or more actions to perform a quality check of themeasurement. A parameter of the measurement such as a signal to noiseratio (action A6) and/or other parameters may be assessed and if theparameter is deemed sufficient (condition C9), for instance signal tonoise ratio above a certain level, the measurement may be consideredsuccessful and the plan may move to the next event. More examples ofquality checks will be described in a subsequent section. If the Event#6 is considered as failed—ie signal to noise ratio not consideredsufficient (condition C10), the plan may go to Event #7 that includetriggering a new calibration (action A7) that comprise one or moreactions and/or events that will not be detailed here for the sake ofsimplicity (for instance, logging in another section of the wellbore)and then, when implemented, go back to Event #4. If the Event #6 isconsidered successful, the plan can move to # Event 8 where the tool maybe pulled out of hole (Action A8) and data may be sent for processing tothe cloud (action A8′) for instance. Of course, this plan is very simplefor the sake of clarity but many different plans can be set up indifferent situations.

As can be seen from the preceding description, the execution of thevarious actions is governed only by logical connections between theevents scheduled in the plan and by preconditions which are detected ornot detected, either at the beginning of, or during, the execution ofcertain actions. The controller executing the plan defined hereintherefore reads the entirety of the plan and can execute it based uponthe logical connections and preconditions set out in steps in the planwhich are governed by events.

A plan of the preferred embodiment can in practice be coupled with anoperational file and a domain file. The operational file can containoperational data about each of the available actions in the domain forwhich the planning is being carried out. The domain file is set out in aPDDL (Planning Domain Definition Language)-based language and containsdefinitions of the environment in which the operation is taking place,in a manner known to those skilled in the art of planning domaindefinition. The plan definition based on the domain and operationalfiles is described in more details in patent application US2017/0370191herein incorporated by reference.

The execution plan may also include different models as will bedescribed in q subsequent section.

The above described process may also be iterated and updated inreal-time. As execution plans are developed and executed or data about aresource becomes available from sources (such as from other measurementsfrom nearby resource sites or the like), the models and/or simulationsmay be updated and recommended candidate services also updated. Whetheror not that model parameter was selected for the execution plan,additional information can be applied to the set of simulation models.In an embodiment, new information can be used to reduce uncertainty andupdate the uncertainty without performing additional analysis ofcertainties. In another embodiment, once the uncertainty of theobjective parameter (for instance, state of the barrier) has beenreduced the service advisor will recommend services to optimize thevalue of the objective parameter (for instance, repair a degradedbarrier). Thus, once the simulations have been performed, results can bestored and further determinations made by terminals/computing systemsthat may have a low computation power relative to the cloud computingplatform 310 thereby mitigating against incurring the costs ofrequisitioning the cloud computing platform 310 again.

For example, if a parameter had an uncertainty in the range of 5-20%with a resolution of 1, 15 simulations may have previously beenperformed to cover the range of 5-20. If new information becomesavailable that the parameter is actually within the range of 15-20, thenthe simulations associated with the range 5-14 can be removed from theset. With a reduced set of simulations, the contribution of uncertaintycan be reassessed and re-ranked. In view of this information, adifferent candidate service targeting a different objective parameter ora different and less costly or less time consuming execution plan may beselected since the most significant source of the uncertainty maychange.

In a particular embodiment, the method may include identifying an atleast one second well integrity service to be performed in the wellborebased on the updated wellbore model, updating the execution plan toperform the at least one second well integrity service, and uponreception of the updated execution plan at the resource site, executingthe updated execution plan. The execution plan may be updated during theexecution of the initial plan. The second well integrity service mayinclude acquiring sensor data using the same set of downhole sensors asfor the previous service having a predetermined parametrization and/orat a predetermined location set that may or may not be same as theparametrization and/or location set of the previous service.

An example of how such model refinement may be used to update theDigital Execution Plan is described in more details below. For instance,in another embodiment shown in dotted line on FIG. 7, if the Event #7 isconsidered successful (condition C9), the data acquired using the sensorand the tool may be sent to the cloud (action A8bis) in # Event 8bis andtool may be stopped at target depth (action A8bis′) until a notificationis received from the cloud server (condition C11).

On the cloud servers 310, once the acquired data are received, the dataare interpreted and the model is refined (for instance as indicatedabove by limiting the range of one or more model parameters at thetarget location thanks to the acquired data). The cloud serverscalculate the updated parameter value and/or uncertainty value of theobjective parameter in view of the refined model. If the uncertaintyvalue is over a certain threshold, the cloud servers may estimate theremaining uncertainty with one or more additional measurements with saidtool (for instance at different target depth) using forecast models (iesimulations) as explained above and recommend one or more options to theend user. If the objective parameter value is under/over a certainthreshold, the cloud servers may recommend one or more options to theend user. If the end user validates one or several of the presentedoptions, the digital execution plan is updated to take into account thevalidated options.

The server then notifies the surface unit 320 when the computations havebeen done and send the updated plan, if any. Based on the updated plan,the tool executes the one or more measurements at the one or more targetlocations—or is pulled out of hole as originally planned in case theupdated plan indicates so. In another embodiment, the user validation isnot necessary: for instance, the user may have entered presetacceptation criteria (gain in uncertainty vs time/cost of the additionalmeasurement) and the options are chosen based on such acceptationcriteria.

Alternatively, based on the results, the end user may initiate a requestof additional measurement and the plan will be updated based on the enduser request.

Furthermore, the plan may not be conditioned to reception of modelrefinement results. It can continue as per the initial plan depending onuser's preference and/or model refinement calculation time and updatewhen a request from the user or model refinement results are received.

For instance, the updated plan may include lowering the tool to a secondtarget depth and subsequent events enabling to perform additionalmeasurements with the same sensor, or if the tool includes severaldifferent sensors, with a different sensor, that are deemed necessary toreduce the uncertainties of the model.

Therefore, the method enables to update the digital execution planautomatically and/or conditionally to end user validation or preferencesin order to optimize model refinement and uncertainties. The plan may inparticular be updated while the tool is in the wellbore based on modelrefinement obtained following to first measurements. Additionalmeasurements may using the tool may in particular be planned based onmodel refinement.

A particular example of plan refinement has been given regarding thedownhole tool. However, the plan takes into account all elements at thewell sites (pumps, winch, etc.) and the updated plan may modify thesequence of events planned not only for the downhole tool but for anyother element at the well site.

Execution Module

Autonomous execution module 430 may conduct various activities such asdata acquisition; computation, parameterization, evaluation, delivery,and contingency planning to be autonomously executed with little to noneed for human intervention. For example, surface operational elementsat a wellsite may be automated (e.g., by live operations module 432). Inan embodiment, wellsite operations such as the deployment of winch,unit, spooling, cranes, pressure control equipment (PCE), sensors, toolmechanics, tool firmware, etc. are automated and controlled based on theDEP. For example, as per the plan, downhole acquisition of sensor datamay be controlled such that downhole sensor data may be capturedautomatically, processed automatically for quality control (QC),interpreted automatically (e.g., using an interpretation library), andincorporated automatically via data transfer (DT) into a customer model(which as disclosed herein may be processed on a cloud or edge server)for simulation and/or decision making. Sensor data may be connected tothe cloud, and an Internet of Things (TOT) architecture may be used toconnect edge device(s) to the cloud. Sensor data may be preprocessed,e.g., transformed to be digestible and refined for simulation.

The autonomous execution module includes controllers that can triggereach element at the well site (including winch, sensors, pumps, etc.) toperform actions and sequences as set in the digital execution plan, aswell as sensors for detecting preconditions, success or failureconditions of the plan.

Edge intelligence (at well site computing device 320) is used wherecertain automated or guided data processing and execution tasks areconducted by a computing system at the wellsite without the need totransfer data to a cloud server. The edge intelligence helps minimizethe amount of data transferred to the cloud server, which may incur aslight delay in time, and allows wellsite operations to continue andrespond to changes in any event of network failure.

In an embodiment, the autonomous execution module receives the digitalexecution plan (DEP) generated by the service advisor and executes theplan using the automated elements/systems/devices of the oil field 200.

In an embodiment, instead of manually performing quality control (QC),the workflow may automate QC via an AutoQC module 434 such that loggingdata may be acquired and processed automatically in real time forprocessing at the terminal/computing device 320 or at a user terminal314 or in the cloud computing platform 310. In an embodiment, LiveQCmodule 436 may obtain real time monitoring data being acquired and maycompare the acquired data to values within a planned job as planned inthe DEP. In the event of an error or problem, an alert or red-flag maybe raised to the user (located at the oil field 200 or remotely), andappropriate actions may be taken to solve the error or problem, afterwhich logging may resume or restart. Suppose, for example, the wellboreis logged in order to acquire ultrasonic waveforms (using an acousticlogging tool) enabling to retrieve information regarding the wellintegrity, and the signal used for processing only being a portion ofthe received waveform, it needs to include all of the informationcontained in the waveforms. Further, if a zone of the wellbore isdegraded, it might be useful to spend more time on such zone tounderstand to which extent it is degraded. During actual operationonsite, if a certain input needs to be changed to obtain desiredresults, for instance, the portion of the processed signal, or if a zoneof the wellbore needs to be re-logged, then decisions and actions may betaken automatically and/or by a domain champion (e.g., data interpreter)and/or a remote operations field engineer monitoring a job and the DEPmay be accordingly updated in real-time or pseudo-real-time.Alternatively, the domain champion or field engineer may validate thedecision being taken automatically within the autonomous executionmodule 430.

Those with skill in the art will appreciate that, while the term“real-time” is used herein to describe some data acquisition and systemscontrol characteristics, “real-time” may also refer tonear-real-time/pseudo-real-time or pre-determined or maximum responsetime, depending on factors related to the architecture of the systems(e.g., drilling equipment, data acquisition systems, and other onsitecontrol systems) used at the oil field 200. In this disclosure, the useof “real time” or “real-time” may refer to actual real-time systemresponses, near-real-time system responses, or systems withpre-determined or maximum response times.

Interpretation and Collaboration

In an embodiment, wireline planning module 410 and autonomous executionmodule 430 interface with collaboration, validation, and learning module450 to engage with a customer or end user, for example to plan, re-plan,calibrate, re-calibrate or interpret collected results in real timeusing one or more interpretation workflows. For example, autonomousexecution module 430 may automatically acquire sensor data downhole(e.g., well barrier data) and automatically process such data.Collaboration, validation, and learning module 450 may use AI-basedmodels to automatically interpret data using an interpretation library452 including one or more interpretation workflows and provide theinterpretation in real time.

Data obtained from the interpretation workflows may be quickly consumedand/or incorporated back into the resource models (e.g., a wellbore/wellbarrier model), which may then be updated for a user to see the impactof acquired sensor data acquired in real-time or previously acquired.Results (e.g., in terms of impact such as sensitivity, uncertainty orcertainty, etc.) from executing the collaboration, validation, andlearning module 450 may be downloaded from a cloud computing platform310 in real-time and may be presented to the user via a user interface.Therefore, system 400 may refine models in real-time and/or adapt dataacquisition in real-time, according to some embodiments. An applicationof real-time adaptation is to impact the acquisition program (or theDEP) as it is being performed at the oil field 200 (e.g., wellsite). Inan embodiment, actual acquired data from the oil field 200 may beincorporated into a model. The model may be subsequently refined suchthat uncertainty associated with a given parameter is reduced or removedin subsequent simulations based on the refined the model or parametervalue is updated. The user may assess results of new data and rely onupdated model(s) to make quicker and more informed decisions. Forinstance, the user may decide in real time to change the DEP to obtainadditional data points, and/or may change operational parameters.

Some interpretation workflows may be automatically performed to refinethe model and the results may or may not be presented to the user. Someinterpretation workflows may be specifically requested by the user. Inthis case, these interpretation workflows are designated “products”. Inan embodiment, there are three levels of interpretation workflows, iebasic, intermediate and final interpretation workflows. At least thefinal interpretation workflows are designated “products”. An example ofbasic, intermediate and final interpretation workflows in the wellintegrity domain regarding measurements taken by acoustic logging toolsare given below: a basic interpretation workflow represents computationor extraction of time travel, amplitude, phase or decay, spectra orother characteristics from the received acoustic signal (for instancefor a pulse-echo measurement), an intermediate interpretation workflowis representative of an acoustic impedance of the material contained inthe annulus based on the computed characteristics of the and a finalanswer product is representative of the type of material in the annulususing the acoustic impedance taken by the pulse-echo measurement butpossibly additional measurement such as pitch-catch measurement andadditional modelling (for instance, Solid Liquid Gas map as mentioned inU.S. patent Ser. No. 10/119,387). Many other examples of interpretationworkflows are available for the same or different measurements in thewell integrity domain (such as casing diameter, casing thickness, casingcorrosion, tool eccentering, casing eccentering, mud acoustic impedance,mud slowness, etc.).

Each product, generally gives access to the client to the value of oneor more updated parameters of the refined model, and is presented to aclient on an end user terminal possibly with an uncertainty. Thepresentation of the product may vary depending on the end user, inparticular in view of the job description or the preferences of theuser.

The collaboration, validation and learning module 450 may include anexample digital space for collaboration, validation, and learning duringor after data acquisition that present data that has been acquired andall or part of the refined model, including updated parameters. Thedigital space, which may be implemented as a collaboration dashboard orsome other digital platform, may bring together people, expertise,software, and processes. In some embodiments, this digital space allowspeople to connect to one another using different user interfaces fromdistant geographical positions in real-time or offline. In someinstances, the space may enable integration and/or displaying of dataacquired at various times, from various sources, that have beenincorporated into the initial model, forming the refined model, whichleads to a traceable and collaborative analysis of the refined model.This digital space, for example, may use data in combination with or inisolation from different forms of data that was previously acquired oris being acquired in real-time. According to some implementations, thedigital space may allow pre-loading of prior data from various sourcessuch as well schematics, drilling data, logging while drilling (LWD)data, wireline logs and cementing information. In one embodiment, thedigital space may display real-time streaming data alongside prior dataas an initial step towards an integrated and holistic analysis. Thedigital space may, in other embodiments, allow a time-lapse analysis ofsimilar data. In some cases, the digital space may use a pre-defined ora customized template(s) either in the same user interface (e.g., abrowser) or in different user interfaces. The digital space in someinstances may allow synchronized displaying of streaming data andinteractions/actions that have been performed or executed on the data.

The digital space may include a collaboration space with certain pointsof interest or focal points (e.g., zones of interest) that helpdifferent people collaborate in arriving at certain conclusions relatedto the state of acquired data and/or processed data. The space may allowtagging inferences on the state of data (quality or outcome orassessment) to a complete set of data or a sub-set of data for aspecific zone or a focal point. This allows cataloging raw data with itsrespective processing, inference/interpretation that can be the basis ofautomation and machine learning.

The digital space may also include a collaborative dashboard (e.g.,digital space) that may allow additional functions such as userinteraction with data and validation of acquired data and results thatmay form the basis of a learning system. The dashboard may interrogateparameters or other values that have led to the final data computation.The dashboard may highlight a specific focal point on acquired data toachieve various functions such as: to flag its state related to thenature of discussion (e.g., data quality, interpretation, inquiry), totrigger a discussion on the focal point, to arrive at a finalcollaborative conclusion on the focal point, and/or to finally resolvethe focal point as validated with consensus on the focal point. Thescope of a focal point may be a small section of acquired data (in depthor in time) to discuss specific and localized anomalies or points ofinterest. The scope may alternatively cover an entire well (e.g., onmatters related to the entire well's data quality or completeness ofinterpretation).

The validated data may be a basis of a learning system and serve as atraining database for data interpretation relative to other wells andacquired later. For instance, data related to the focal point may bestored in a structured manner.

Further, the collaborative dashboard may allow to have access to thefinal deliverable (computation) but also the raw data in order to use QCtools on the raw data, to change manually one or more parameters of thewell or tool model and simulate deliverables with the raw data andupdated model parameter, to use a different processing on the raw data,to comment results or integrate results of multiple logging tools usedin a same section of a well on a same dashboard.

In some instances, collaboration, validation, and learning module 450may work in conjunction with wireline planning module 410 and autonomousexecution module 430 to use artificial intelligence (AI) and/or machinelearning, and/or deep learning processes, for example, to incorporatedata back into a model and/or update parameters of the model inreal-time. For example, validating a parameter and/or an updatedparameter associated with a model may include using acquired data and/orother validated/updated parameters in a machine learning tool and orartificial intelligence tool. The autonomous system may be open to thirdparties such as a client who may develop products/services incollaboration with a service provider/a service advisor (e.g., serviceadvisor discussed in association with FIG. 1) and may have access tomodels, sensor data, metrology data, etc. In some implementations,real-time, collaborative dashboards may be implemented on a cloudinfrastructure for data monitoring and automated processing. In anembodiment, catalogs (e.g., reference models) may use labeled focalpoints stored in the interpretation library 452.

Example Relative to the Well Integrity Domain

Suppose, for example, a client wishes to accomplish an objective such asdetermining a certain parameter of a well barrier (e.g., the degree ofcasing corrosion and/or composition of material in the annulus). In thisexample, a client may provide casing corrosion of the barrier as anobjective parameter. To achieve this objective, according to anembodiment, the client may use a client terminal to provide knowncontextual information about the barrier, such as prior knowledge ofcurrent borehole fluid, pipe behavior, and tool string. A serviceprovider, taking input from the client, may use a cloud server to buildan initial model of the barrier based on prior knowledge of the barrier.The service provider may also enter its own prior knowledge of thebarrier via a service provider terminal in building the initial model.In some cases, a barrier model may simply be provided by the client orlocated from a database of the service provider.

Based on the initial model and the objective parameter, the cloud servermay generate multiple wireline candidate services for determining theobjective parameter. Some or all of the candidate services may bepresented to the client for selection, for example, with additionalinformation such as an uncertainty associated with each service, priceof the service, descriptions of the service, etc. The client may selectan effective service among the multiple candidate services. The cloudserver may build a digital execution plan (DEP) specifying operationalparameters of the selected service, and may send the DEP to a oil field200 (such as terminal 320) for autonomous execution of the DEP. At thewellsite, for example, certain sensor data regarding the barrier may beacquired using wireline tools deployed downhole. Automated qualitycontrol of acquired data is also performed at the wellsite. Duringwellsite execution, the DEP may be modified as needed, for example, ifacquired data has poor quality. The model of the barrier may also berefined based on acquired data. The wellsite system may send acquireddata to the cloud server, which may use the acquired data to compute theobjective parameter, now with reduced uncertainty. The determinedobjective parameter may be displayed to the client on the clientterminal in the form of an answer product. Therefore, the disclosedwireline service workflow may be considered an end-to-end (from sensorto customer) implementation. Following such a workflow allows the clientto achieve various objectives faster, with better results, and fromflexible locations, which in turns helps the client's decision makingregarding oil and gas operations at the wellsite.

FIG. 6 illustrates an example wellbore (or well barrier) model 550 whichmay include a representative understanding of the state of a wellbore,such as a state of the borehole fluid, a state of the annulus, and astate of one or more casings (ie pipes). As shown in FIG. 6, wellboremodel 550 may be derived from prior information such as a completionprofile or design (parameterized grid 552), borehole fluid(parameterized grid 554), and cement information (parameterized grid556). The wellbore model may include a model of the pipes (558), a modelof the expected mud profile (560) and a model of the expected annulus(562). The model that has been presented is relatively simple as itcomprised two nested casings 558 a, 558 b, annulus 562 at the externalborder of each casing (each casing being cemented to the formation afterbeing set) and borehole fluid 560 in the inner casing. The boreholefluid and annulus model may include a plurality of zones 560 a, 560 b,respectively 562 a, 562 b, 562 c depending on thenature/property/composition of the fluid, wherein the property of thematerial are considered homogeneous in each zone, for intance. It is tobe noted that more complex models may be created, for instancecomprising more pipes, annulus and borehole fluid zones, eccenteredpipes, etc. Parameters may be associated with each zone of the boreholefluid and annulus depending of properties of such zones (mud velocity orslowness, density, or composition of the material in the annulus forinstance). Parameters may also be associated with each pipe 558 a, 5558b (diameter, thickness, eccentering, etc.).

In an embodiment, an wellbore model 550 may be derived from informationwhich includes but is not limited to: a completion design or wellschematics detailing the pipe and borehole geometry and specificationsincluding restrictions; well deviation survey; the location and state ofwell barrier(s); borehole fluid inside the tubulars and/or the annulus;well barrier properties such as cement; recent wellbore operations suchas pressure testing; etc.

An example of the system 400 applied to well integrity may have abarrier to barrier approach. The modules of system 400, includingwireline planning module 410 (sometimes called a service planning moduleas it includes a service advisor), autonomous execution module 430, andcollaboration, validation, and learning module 450, apply to thisembodiment as will be explained in details below.

Planning may start with an initial state of a well barrier as theresource model. For example, a client or a service provider may generateor populate a model such as a well barrier model in wireline planningmodule 410. The well barrier module may include information about theconfiguration of the well (well geometry), composition of borehole fluidand of cement, recent well operations, well deviation and state of thecasing or cement if any information are available. The client operatorhas the ability to determine the current (or most recent) informationrelated to a documented state of a well barrier. The determination maybe done either manually by entering the current state of the wellbarrier, or done through an automated system (e.g., by automaticallypopulating the state from a well barrier model, such as retrieved from arepository, or a similar database at the front end, such as by a proxymodel).

With reference to FIG. 7, in an embodiment, the wireline planningworkflow 410 may include consulting a state of wellbore barrier (block602) based on the available information of one or all of the clientwellbores and determining from the state of the barriers a barriersurveillance prioritization (block 604) determining where wellboresurveillance is needed the most, typically where information is missingor where a degraded state of the well barrier has been reported orsuspected.

Where a wellbore has been prioritized for surveillance, astate-of-the-well (well barrier) model is set up based on the currentwell information as explained in relationship with FIG. 6 (block 606),and client objectives related to the barrier surveillance are determined(block 608). As mentioned above, the objective parameter may be a stateof the barrier, a casing corrosion indicator and/or a materialcomposition in the annulus and the client may seek either to reduceuncertainty regarding the objective parameter or to optimize value ofthe objective parameter. Client objectives may be stated, expressed,selected, or otherwise entered using interface on end user terminal.

As an example, the initial state of the well barrier may have a numberof states, e.g., a state that meets accepted barrier criteria, adegraded state, or a failed state that does not meet the acceptedbarrier criteria. The workflow enables barrier surveillance thatmonitors the state of a well barrier to ensure that the well barriermeets and continues to meet acceptance criteria. In the case ofdegradation, the workflow monitors the level of degradation of the wellbarrier and potential severity of degradation. There may be a relativelyhigh level of uncertainty on the state of a well barrier and itscapacity to perform its designed functions and the objective of theclient may be to reduce such uncertainty. In the case of an identifiedfailed barrier, the workflow may identify the location of failure toallow an appropriate plan of remedy or intervention.

Different technology options are ranked with the service advisor (block610) that will enable to fulfill client's needs as explained in detailsabove. In an embodiment, each option is associated with a rationaleexplaining why a recommendation meets the stated objective or will notdeliver an optimal solution for one or more of the client objectives.

In an embodiment, the service advisor ranks the options (ie recommendedservices) according to client objectives and priorities (608)—throughsimulations (ie Global sensitivity analysis and/or forecast models ofdifferent candidate services) that are focused on objective parameter(value and/or uncertainty) optimization as well as on a multi-factorranking logic (612) taking into account different parameters including:stated objective(s) of the client; the suitability, effectiveness,and/or efficiency of a specific technology in delivering on the statedobjective(s) and in the specific operating environment (e.g., bestpractices in managing specific scenarios considering parameters such asborehole fluid attenuation, pipe thickness, pipe diameter, signal tonoise ratio (SNR), etc.): as well as other factors such as costs andavailability for instance.

In an embodiment, client objectives may be established client workflowsthat dictate the choice of technologies and processes (e.g., zonalisolation or casing cut & pull, as used by the client operator or by aservice provider), may be specific tasks in isolation or in combination(e.g., pipe inspection or cement evaluation). In this case, the ServiceAdvisor may not be used by the client (if the client procedures havealready identified a service) and/or may be greatly constrained by theclients constraints to output recommended services and/or technology.

Based on the output of ranking block 610, a client operator may make aninformed decision in selecting the recommended technology by consideringtradeoffs between cost and benefits. The technology is then selected bythe client based on the service advisor service ranking (block 614), andbased on client's decision a digital execution plan (DEP) is set (block616), both in order to mitigate the uncertainty (617) of and/or optimizethe initial state-of-the-well model.

The Digital Execution Plan is set up based on different elementsincluding-a wellbore physics model (block 618) that can be or includethe state-of-the-well model (606), a tool parameters model (block 620),a domain quality control model (block 621), and an acquisition sequenceplan (block 622). The DEP is indeed set up so as to adapt to manycircumstances, including contingencies, acquisition problems andrefinement of the well model, as will be explained in more detailsbelow.

In an embodiment, the DEP is set up completely automatically. In otherembodiments, the DEP is set up automatically and optimized/validated bydifferent stakeholders. For instance, using a planning dashboard, a wellintegrity subject matter expert SME (e.g., end user) may utilize thesystem 400 to validate a digital execution plan DEP. A drilling or assetmanager may work with a well integrity SME on an interactive planningplatform by considering various planning scenarios, to which sales andcommercial personnel may also contribute. Elements of the DEP that maybe of particular attention are location of measurement and/or loggingspeed.

The DEP 616 may is customized for each wellbore based on a predictedstate-of-the-well model (606), client objectives (608), and the choiceof technology (614). The DEP 616 may be provided in the form of a datastructure.

Once finalized and/or validated, the DEP is communicated to the wellsite, for example at an edge server, in connection with autonomousexecution module 430, guides the autonomous execution of a wirelinelogging plan.

Autonomous execution module 430 includes performing an autonomousacquisition (block 632) to make sure that the tool has correctly worked(signal has been fired, received, etc.) and captured the essentialinformation relative to the barrier element(s). In particular, at theresource site, an orchestration system (sometimes provided by an edgeprocessing device) may receive the DEP and control a data acquisitionsystem based on a DEP schema. For example, the edge during acquisitionexecutes planned passes (according to the DEP) with the pass specificacquisition and computation parameters stipulated in the DEP as will beexplained in more details below. The

In an embodiment, data acquisition parameters according to the DEP areat best estimates. Therefore, the method may include automatedvalidation or quality check (QC) performed by an automated QC systemprovides real-time validation and corrective actions for raw dataacquisition (ACQ QC)—not shown on FIG. 7. QC is performed regularly toimprove the data acquisition parameters. In some embodiments, when theplan includes several logging passes, QC′d parameters are subsequentlyused and passed on to subsequent passes.

The autonomous acquisition (block 632) is obtained in particular bycomparing the obtained acquisition versus the domain QC model. Theintegrity of data acquired is paramount as any issues in the recordingof data irrevocable. For well integrity, the measurements that are oftenused are acoustic measurements, based on pulse-echo or flexuralmeasurement, as explained in relationship with the hardware. For suchmeasurement, example of acquisition parameters that are monitored andevaluated will be described in more details in relationship with FIG. 8.

Once the acquisition has been correctly set, the method comprises anautonomous computation (block 634) deriving one or more target wellboreparameters based on the acquired information. Such autonomouscomputation takes into account an autonomous (tool and wellbore)parameterization (block 636). The autonomous parametrization optimizesthe other parameters of the model and tool. The autonomous computationenables to automatically interpret data to obtain parameters relative tothe state of the barrier such as casing corrosion indicator andcomposition of the material in the annulus based on the data acquired bythe tools used in the wellbore and processing techniques that are notdetailed here. Such processing techniques may include in particularartificial intelligence. In a particular embodiment, the computation isobtained based at least on a first characteristic measured in thewellbore.

The autonomous parametrization (636) optimizes the other parameters ofthe model and tool. The autonomous parametrization (636) includes anautomated quality check of the computation parameters obtained at block634 that will be explained in more details in relationship with FIG. 8.

As discussed above, the autonomous computation may be refined based onupdated parameters in the autonomous parametrization and/or applied thewellbore model refined thanks to the autonomous parametrization may beapplied to the subsequent logging passes of the tool in the wellbore.

If certain predetermined criteria are met by the acquired data, anautonomous contingency plan (block 638) may be triggered as will beexplained in more details below.

The method may also include an autonomous evaluation (640) for verifyingthe validity of the results obtained from the computation. Suchautonomous evaluation may verify the computation by checking if thevalues of the wellbore parameter resulting of the computation areconsistent with other related results obtained via downhole measurements(for instance, casing corrosion indicators obtained by acoustic andelectromagnetic logging and/or caliper if both are available). In otherwords, the autonomous evaluation comprises using a second characteristicrelative to the wellbore parameter that is measured in the wellbore andvalidating the computed wellbore parameter based on a correlationbetween the computed value of the wellbore parameter and correspondingsecond characteristic. A confidence index may also be generated duringautonomous evaluation based on the correlation.

Alternatively or in complement, the autonomous evaluation may correlatethe statistical distribution of the data points of the firstcharacteristic (for instance, acoustic impedance) used to compute thewellbore parameter in all or part of the wellbore with typicalstatistical distribution for such data points in other wellbore toevaluate the likelihood of the computed wellbore parameters. Thedeliverables are delivered autonomously to the user that is generallysituated remotely from the well site (block 642), for instance accordingto DEP schema that stipulate select deliverable components. Thedeliverables may also be delivered to a collaboration platform (ie liveproduct dashboard) where several SME may collaborate to analyze theobtained results. On the live product dashboard, performance assessmentversus metrics and analysis of results may for instance be conducted.

An example of a DEP 616 for the above-mentioned method is disclosedthereafter in relationship with FIG. 8A

In an embodiment, the plan may include a well physics model 618 relatedto physical parameter (eg properties) of a wellbore (e.g., resonancefrequency range, pipe dimensions, mud impedance and velocity) asexplained above. It may also include a parametrization model 620including a parametrization of the tool (acquisition parameter values)and a parameterization of the resulting computations (computationparameter values). Acquisition and computation parameter values may becustomized as a function of wellbore physics model related to physicalproperties of a wellbore (e.g., resonance frequency range, pipedimensions, mud impedance and velocity) at the planning stage. Forinstance, tool parameters (e.g., firing voltage, window settings,filters for the acoustic logging tool) (620) that affect raw dataquality (e.g., waveforms, SNR for the acoustic logging tool) arecustomized. Such customization impacts or even controls the quality ofdata acquisition from the tool as will be explained below.

The DEP may also include a domain quality control model 621 that may becustomized at the planning stage. The definition of accepted versusfailed states (or acceptable versus undesired states) during acquisitionof raw data and/or computation of processed results are articulated. Inparticular, when expected characteristic of the acquired data are storedin the domain quality plan.

The DEP 616 may also include a sequence 460 of one or more loggingpasses, e.g. a sequence of actions to trigger relative to one or moreequipment at the well site. Such plan is customized/updated to meet notonly computational workflow requirements but also to address dataacquisition requirements that may need to be improved for differentsections of a well (e.g., due to changes in fluid or pipe schema). TheDEP may for instance include a specification for the number of passes(1, 2, . . . N) corresponding to 622. The pass specification indicatesstarting and stopping depth and whether the pass is in the up or downdirection for each pass.

For each pass, the structure of the sequence 460 at high level may besimilar: it may include a logging event 464 comprising a plurality ofactions (such as triggering the downhole tools and/or sensor, moving thedownhole tool, performing the computation, etc.). Such logging event mayinclude for instance autonomous acquisition 632 as per FIG. 7 diagram.The logging event and calls the pass specification 622 andparametrization model 620 and possibly the wellbore physics model 618 toperform the logging pass as per the plan. The passes may be full or apseudo collection that measures some data and estimates or interpolatesother data.

Once the logging pass is over (for instance the logging event having oneor more success conditions including that the tool has reached thestopping depth), the DEP may include validation event 466 for saidlogging pass that includes automated data quality control foracquisition parameters. Such event may call the domain quality controlmodel 621 and possibly parametrization 620 and/or wellbore physics model618 if the domain quality control model depends on such models. If thevalidation event indicates that the expected data was not collected(failure condition), e.g. that one or more of the characteristic of theacquired data does not correspond to the expected characteristic as perthe domain QC model 621, the DEP may include a contingency plan 467 toexecute, based on contingency guidelines 469 contained in the plan(corresponding to a troubleshooting sequence as disclosed inrelationship with FIG. 5). If the expected data was collected, thesequence goes to implementation of logging pass i+1.

Examples of quality control operations regarding acquisition parametersof wireline acoustic tools are given below. Some of the quality controlfor acquisition parameters explained below in relationship with acousticlogging tools could be applied with modifications that can be determinedby the one of ordinary skill to other logging tools.

First examples of monitored acquisition parameter are relative to awaveform integrity. Indeed, in ultrasonic acquisition (pulse-echo orflexural measurement), the entire waveform is not recorded andtransmitted to the well site for processing, only a portion of it isgenerally recorded. Therefore, the time window during which the signalis recorded must be correctly set. However, depending on severalparameters of the well (ie hole size, borehole fluid properties and inparticular slowness, eccentricity), this time window can vary from a jobto another or even a portion of the well to another. Such parameter istherefore monitored as part of the autonomous acquisition. In anembodiment, as part of the DEP, a default time window is set based onthe well parameters that are known. The default time window may be setbased on a waveform simulator that takes into account the wellboreparameters. The autonomous computation includes verifying the relevanceof the time window by checking the portion of the waveform obtained inthe time window against a waveform model as part of the domain QC model.In an embodiment, this operation may use machine learning and classifiesa set of received waveforms in view of a training dataset in theacceptable or undesired quality set. If more a predetermined proportionof the waveforms are classified in the undesired quality set (forinstance 5%), the quality is set to undesired. In another embodiment,quality of cross-correlation with a single synthetic reference trace maybe obtained and the acceptable or undesired quality is determined by theamount of cross-correlation. In such embodiment, the single referencemay be obtained based on similar wellbore conditions, for instance bythe ultrasonic simulator. If the quality is considered undesired, thetime window is changed as part of the autonomous computation. The timewindow may be increased/decreased by simple iteration or the domain QCmodel may include a module for calculation of updated time window. Inthis case, the DEP may be changed as well, in order to check for therelevance of the updated time window.

To validate the waveform integrity, additional or alternativecomputation may be made. For instance, the group delay may be checked atraining databased or a group delay of a simulated waveform. As areminder, the group delay is defined as the first derivative function ofthe phase of an energy spectrum with respect to the frequency of theenergy spectrum and, in wellbore integrity application, enables tolocate the resonant frequency of the casing as it is the frequency atwhich an energy loss occurs, ie the frequency at which the group delayis maximum.

When the chosen tool to perform the service comprises two measurements(pulse-echo and flexural), the time window for one of the measurement(generally flexural) may be deduced from the time window set andverified for the other measurement (pulse-echo) using as wellinformation based on the well physics model (and of the tool model.

Second examples of monitored acquisition parameter are relative to asignal-to-noise ratio. Indeed, the signal to noise ratio is optimal toensure that the signal is neither significantly attenuated norsaturated. A monitored parameter may be the acquisition gain channels orthe noise floor. The domain QC model may comprise threshold for someparameters that determine if the monitored parameter is in a acceptableor undesired state. For instance, a threshold may be set on a absolutevalue or on a ratio (for instance a ratio between the values of twochannels). As part of the autonomous acquisition, the signal strengthmay be dynamically readjusted and/or different filters may be activatedfor removing a type of noise.

When all of the logging passes as per the plan have been performed, thesequence 460 moves to an interpreting event 468 including interpretingthe acquired data to compute one or more wellbore model parameter valuesand/or uncertainties (corresponding to 634 of FIG. 7). Theinterpretation is made using the parametrization model 620, inparticular the computation parameters and the wellbore model 618, thatincludes one or more predetermined wellbore parameters that are used tocompute the computed wellbore parameter. As an example, the casingparameters that are computed based on the acquired data may also dependon borehole fluid parameters such as borehole fluid slowness. Once theinterpretation event has successfully occurred, the sequence moves tovalidation of the interpretation 470. Such validation includes a qualitycontrol of the interpretation that compares at least one characteristicderived from the computed wellbore model parameter values and/oruncertainties to an expected characteristic defined in the domain model621. The quality state of the data is determined either as acceptable orundesirable. This event may correspond to the autonomousparameterization 636 of FIG. 7.

In an embodiment, the quality control includes determining change pointsin the computed wellbore model parameters and deriving change points inthe one or more predetermined wellbore model parameter. The derivedchange points for the one or more predetermined wellbore model parameterare compared to expected change points. In particular, the one or morepredetermined wellbore model parameters are relative to borehole fluidand/or casing internal diameter and/or casing thickness. In other words,the quality control includes detecting abrupt changes in the pipethickness or borehole fluid, using change points techniques. As anexample, pipe thicknesses zone are identified with the change pointtechnique using the pipe thickness measurement. Thereafter, thethickness of the pipe measured at a predetermined depth or the averagethickness measured in the zone is compared to the thickness that isknown from the wellbore model. The thickness is compared to expectedthickness to understand if the data quality is acceptable orundesirable. If the thickness deviates from the model in at least thezone or in a plurality of neighbouring depths, the wellbore model 618(well profile) may be updated, for instance by updating a casinginternal diameter. Similarly, the mud slowness results are monitored.For instance, slowness versus depth is plotted and change points wherethe slope of this quantity changes (of more than a predeterminedpercentage) are flagged (with collar location excluded if they are knownalready). For each change point, it is verified if the change pointoccurs at a collar location in which case the change of slope may be dueto the pipe parameter and the pipe parameters are verified. If there isa change in the pipe parameters, the change point is discarded. Once thefinal set of change points is obtained, mud zones in which the mud is ofthe same type are set so as to be delimited in depth by two adjacentchange points. The mud (or borehole fluid) may be classified (forinstance as oil-based, water-based, etc.) based on a representativeslowness value in the zone (for instance, minimum, maximum and/oraverage in the zone) and mud composition in each zone is compared toexpected mud composition.

Other examples of quality control may comprise normalizing theprocessing techniques by checking the obtained results versus reality ina zone that is well-known or, when two measurements or two differentprocessings giving access to the same parameter (for instance, acousticimpedance) are available, the results of one measurement versus theother measurement. The value of a wellbore parameter (for instance, mudimpedance) or of a tool parameter (for instance, flexural offset) maytherefore be updated as a result of the normalization if there is adiscrepancy. The normalization may be repeated in several depths of theborehole, in order to take into account variations of wellbore model, inparticular of mud properties, with depth.

Alternatively, the interpretation and/or associated quality controlcould alternatively be performed remotely from the resource site, whichwould trigger additional transmission operation.

When validating the interpretation returns an acceptable state, thecomputed wellbore parameter values and/or uncertainties are updated 472,which may include transmitting them to a remote server (e.g. at alocation remote to the resource site). Additional data may betransmitted as well, such as the raw measurements, for instance ifrequested by the user. Once transmitted, they are generally displayed tothe user.

When validating the interpretation returns an undesirable state, thesequence includes performing a contingency plan 467. The contingencyplan 467 may include one or more actions defined at the planning stagebased on the outcome of the validation. It may include updating 474 thewell physics model 618 and/or parametrization model 620, for instancethe predetermined wellbore parameters or tool parameters of undesirablequality. It may also include performing additional computations and/oradditional logging passes 476, to acquire the data with the same ordifferent parametrization at the same or a different location, and/or toperform additional calibration of the tool. Such operations are listedin the diagram of FIG. 7 as autonomous contingency planning 638.

In an predetermined embodiment, a contingency plan may includedynamically updating the DEP by adding contingency passes to the DEPusing predefined contingency passes parameters. Such contingency planmight include passing with a slower logging speed in a degraded zone ortriggering an additional sensor upon meeting of a predeterminedcriteria. The contingency may dynamically activate contingency passesfor relevant sections using pre-configured contingency pass parametersfor a specific contingency, and such passes are added to the processingpipeline.

A schematic diagram of logging passes is provided at 480, on FIG. 8B. Afirst pass is illustrated starting proximal the top of the well andextending for a first region. The first pass is downward in this regionand sensor data is collected on the pass. The second pass is illustratedin the first region and in the upward direction. The third pass isillustrated from proximal the top of the well and extending into asecond region. In an example, the data may not be validated and then,according to a contingency plan, a fourth pass extends from the end ofthe third pass into a third region of the well. A fifth pass may beconducted from the third region in an upward direction to a top of thewell.

The DEP is not a fixed but adaptative sequence: it enables to customizethe tool parameters in view of the results obtained in the wellboreand/or to perform additional operations/modifying a number of loggingpasses based on the acquisition or computation results and the domainquality control model and possibly the parametrization and/or wellphysics model. A DEP may be refined automatically at different stages ofthe plan and/or manually by a user situated at the well site orremotely.

The disclosed principles may be combined with a computing system toprovide an integrated and practical application to achieve autonomouswell integrity operations. For instance, the disclosed systems andmethods improve client/user experience. As an example, system 400 mayprovide access (e.g., credential access) to a client, allow the clientto locate an asset or assets associated with the oil field 200, andallow the client to identify or capture client objectives such asparameters of uncertainty; derisking elements, limiting uncertaintyrelative to and optimizing a well barrier, etc. In some instances, theclient either may provide or select a customer model or may entercertain data that allow the system 400 to build a proxy model (e.g., byimporting data from a preexisting well). The system may determine theclient objectives such as uncertainty, and the client may make decisionsbased on the determined client objectives. As another example, system400 may grant more flexibility to a client by allowing the client tolook up (e.g., within the client's own database or knowledge base orfrom other sources available to the client) models or contextualinformation in order to plan optimal services that meet the client'sobjectives. A list of options (e.g., parameters, models, executionplans, etc.) may be created, ranked, and made available to the client toallow the client to make real-time, informed decisions on the choice oftechnologies and/or services (e.g., to add, remove, or change) in orderfor the client's objectives to be achieved. For example, if importantinformation about a well barrier is missing from a current wellbore, alogging service may be performed in this wellbore. Oil field operations,for example, may be changed dynamically during data acquisition. Thequicker response reduces or eliminates waste in time and resourcesassociated with approaches where sensor data is acquired (without anyreal-time monitoring or feedback) and only analyzed at a delayed time.Now, with the integrated approach presented in this disclosure, sensordata may be updated (e.g., using an Evergreen model) that is regularlymonitored to determine if results are as expected. Data acquisition maybe changed in real-time if results are not as expected.

According to some implementations of system 400, products (e.g.,candidate services) may be proposed and delivered in real-time with theaid of interpretation library 452 translating the candidate services andexecution plans into consumable data such as result plots and otherinterpreted forms/interpreted results and presenting the consumable data(e.g., interpreted results) in real-time or pseudo-real-time via acollaboration dashboard accessible by one or more users simultaneouslyand/or via similar or dissimilar display devices and/or via userterminals associated with the respective users. This beneficially offerssignificant advantages over approaches where products/services aredelivered only after advanced post-job analysis of data using otherinformation available about the oil field 200 (e.g., wellsite and/or oilfield). In some instances, whereas a manual process may take days to getto a reasonably accurate result, and the turnaround time of days may betoo late for customers/users to make informed decisions on the wellsitewhile producing and maintaining the integrity of the wellbores at theresource, the systems and methods disclosed herein decrease, in someembodiments, the wait time to make such site planning decisions in arelatively short amount of time (e.g., 2-50 minutes, 1-2 hours, 1-8hours, 1-12 hours, etc.)

System 400 has been described in relationship with acoustic loggingtools. Well integrity services include other types of tools (such aselectromagnetic tools and/or calipers) and the methods and systemsdescribed therein are applicable to such tools.

The current disclosure relates to a method for monitoring a well barrierof a wellbore formed in a geological formation at a resource site. Thewell barrier includes at least a casing and an annulus situated betweenthe casing and the geological formation. The method comprises receivinga model of the wellbore including a plurality of wellbore modelparameters respectively having wellbore model parameter values and/orwellbore model parameter uncertainties. The method also includesidentifying at least one well integrity service to perform in thewellbore with a downhole tool. The well integrity service includesacquiring sensor data relative to a state of the well barrier. Anexecution plan, including a sequence of actions to perform the at leastone well integrity service is then generated based on the at least onewell integrity service. The execution plan is executed at the resourcesite, which includes controlling operation of one or more equipments,including the downhole tool, at the resource site to perform the atleast one identified service. Based on the sensor data acquired at theresource site during the execution of the execution plan, the methodincludes updating the value and/or uncertainty of at least one of thewellbore model parameters, thereby updating the wellbore model anddetermining a state of the well barrier based on the updated wellboremodel.

The well integrity service may include acquiring sensor data relative toa state of the well barrier using a predetermined (tool)parametrization. The tool parametrization may include a voltage includesa voltage, an intensity, a gain, acquisition filters, acquisition timewindows.

In an embodiment, the downhole tool includes one or more wirelineacoustic tools and acquiring sensor data includes acquiring one or morewaveforms obtained in response to an emitted signal.

In an embodiment, executing the at least one well integrity service.includes interpreting the acquired sensor data to compute the one ormore updated wellbore model parameter values and/or uncertainties. Inparticular, when the well integrity service includes an acousticservice, interpreting the data includes computing characteristicsrelative to the waveforms, including a travel time, amplitude, phase,decay, spectra.

In an embodiment, the wellbore model includes at least one of a boreholefluid model, a casing model and an annulus model. The wellbore model mayfor instance include includes information regarding one or more of:wellbore profile, complete profile, borehole fluid properties, recentwellbore operations, annulus properties, a state of a casing, and astate of an annulus.

The method may include receiving an objective parameter associated withthe state of the well barrier and computing an objective parameter valueand objective parameter uncertainty using the wellbore model. Theobjective parameter is a function of at least one wellbore modelparameter. The at least one well integrity service is then identifiedbased on the wellbore model and on the objective parameter

In an embodiment, identifying the at least one well integrity serviceincludes simulating variation of the value and/or uncertainty of atleast one of the wellbore model parameters according to one or morescenarios, and computing a forecasted objective parameter value and/orforecasted objective parameter uncertainty associated with eachscenario. The at least one well integrity service is identified based onthe forecasted objective parameter value and/or forecasted uncertaintyassociated with each scenario.

In an embodiment, identifying the at least one well integrity serviceincludes determining whether the at least one well integrity servicereduces the uncertainty of the objective parameter based on theforecasted objective parameter uncertainty associated with eachscenario.

In particular, the method may include determining, based on at least asubset of the plurality of scenarios, sensitivity data regarding therespective contributions of the wellbore model parameters to theobjective parameter; and identifying based on the sensitivity data, oneor more target wellbore model parameters having high contributions tothe objective parameter. The at least one well integrity service isidentified based on the target wellbore model parameters. This may alsobe designated as a global sensitivity analysis.

In another embodiment, each scenario of at least a subset of theplurality of scenarios is representative of performance of an availableservice, and simulating the variation of the wellbore model parametersvalues and/or uncertainties according to said scenario includesincorporating data representative of the performance of an availableservice into the wellbore model. This has been described in more detailsin the specification also as forecast models. In this case, simulatingthe variation of the wellbore model parameters values and/oruncertainties according to said scenario is based on one or moreequipment models; in particular the downhole tool for performing theavailable service. The equipment model may take into account one or moreequipment parameters representative of one or more of accuracy,calibration and/or reliability of the equipment.

In another embodiment, the method comprises determining, based on afirst subset of scenarios, sensitivity data regarding the respectivecontributions of the wellbore model parameters to the objectiveparameter and identifying based on the sensitivity data, one or moretarget model parameters. A plurality of candidate services areidentified based on the target model parameters. The method comprisesafter that simulating the variation of the wellbore parameter valuesand/or uncertainties according to a second subset of scenarios. Eachscenario of the second subset is representative of performance of one ofthe candidate service, and the simulation according to said scenarioincludes incorporating data representative of the performance of theavailable into the wellbore model. The at least one well integrityservice is identified based at least on the forecasted objectiveparameter values and/or uncertainties associated with the second subsetof scenarios.

The objective parameter may be an indicator of a casing corrosion, or ofa composition of the annulus.

Identifying the at least one well integrity service may includepresenting a set of recommended services to an user based on theforecasted objective parameter values and/or uncertainties associatedwith the plurality of scenarios. Each of the recommended services may beassociated with additional information that includes one or more of thefollowing: a price of the recommended service, and a description of therecommended service and possibly an indicator of an uncertaintyassociated with the objective parameter, for scenarios corresponding tothe recommended services. The at least one effective service is selectedfrom the set of recommended services by the user. In particular, amulti-factor logic may be used to identify and/or rank the recommendedservices.

In an embodiment, the at least one well integrity service is an at leastone first well integrity service. The method may further includeidentifying an at least one second well integrity service to beperformed in the wellbore based on the updated wellbore model, updatingthe execution plan to perform the at least one second well integrityservice, and upon reception of the updated execution plan at theresource site, executing the updated execution plan. Naming theexecution plan an initial execution plan, the execution plan may beupdated during the execution of the initial execution plan.

The at least one first well integrity service may include acquiringsensor data using a set of downhole tools having a first predeterminedparametrization and/or at a first predetermined location set and the atleast one second well integrity service may include acquiring sensordata using the set of downhole tools having a second predeterminedparametrization and/or at a second predetermined location set.Alternatively, the at least one second well integrity service mayinclude using a different set of downhole tools than the set used forperforming the first service.

In an embodiment, executing the execution plan includes performing aquality control based on the acquired sensor data. The quality controloutputs a state that is determined as either acceptable or undesired bycomparing a characteristic derived from the acquisition data with anexpected characteristic. The automated quality control is performedbased on a QC model included in the execution plan an included theexpected characteristic and/or acceptable and/or undesired state. The atleast one wellbore model parameter may be updated based on the acquiredsensor data when the automated quality control outputs an acceptablestate.

In an embodiment, the automated quality control includes controlling aquality of one or more acquisition parameter used during dataacquisition. The acquisition parameters include one or more toolparameters, for instance selected from the group consisting ofacquisition time window, a gain, a filter, a voltage, an intensity. Ifthe at least one well integrity service includes an acoustic service,performed with one or more acoustic logging tools as mentioned above,the data quality control may relate to waveform integrity orsignal-to-noise ratio.

When executing the execution plan includes interpreting the acquiredsensor data to compute one or more updated wellbore model parametervalues and/or uncertainties, the data quality control may includecontrolling a quality of one or more computation parameter used duringdata interpretation. In this case, the one or more computed wellboremodel parameters values and/or uncertainties are computed taking intoaccount on one or more predetermined wellbore model parameter. Thequality control may include determining change points in the computedwellbore model parameter values and deriving change points in the one ormore predetermined wellbore model parameter values. The derived changepoints for the one or more predetermined wellbore model parameter valuesare compared to expected change points. The one or more predeterminedwellbore model parameters may be relative to borehole fluid and/orcasing internal diameter and/or casing thickness. If the state of thequality control is undesired, at least one predetermined wellbore modelparameters may be updated.

More generally, executing the execution plan comprises, in response toobtaining an undesired state from the quality control, performing acontingency plan that may include one or more of updating wellbore modelparameters, updating a parametrization of the downhole tool, andupdating the sequence of actions of the execution plan.

Each action of the sequence of the execution plan is associated with oneor more preconditions, success conditions and failure conditions.

In an embodiment, the execution plan is generated remotely from theresource site on a set of one or more processors and is transmitted tothe resource site for execution at the resource site. At least one ofthe acquired sensor data and updated wellbore model parameter maytransmitted from the resource site to the one or more computing systemprocessors. A visualization representing the state of the well barriermay also be generated and presented to at least one end user on an enduser terminal located at one or more locations, including at theresource site and a remote site.

When the method includes a quality control, the acquired sensor dataand/or the at least one updated wellbore model parameter is transmittedfrom the resource site to the one or more computing system processorswhen the automated quality control outputs an acceptable state.

The disclosure also related to a system for monitoring a well barrier ofa wellbore formed in a geological formation at a resource site. The wellbarrier includes at least a casing and an annulus situated between thecasing and the geological formation. The system comprises one or morecomputing system processors configured for receiving a model of thewellbore including a plurality of wellbore model parameters each havingat least one of wellbore model parameter value and associateduncertainty and generating an execution plan including a sequence ofactions to perform at least one well integrity service based on thewellbore model. The at least one well integrity service includesacquiring sensor data relative to a state of the well barrier with adownhole tool. The one or more computing system processors is alsoconfigured to execute the execution plan at the resource site, whereinit includes controlling operation of one or more equipments, includingthe downhole tool, at the resource site to perform the at least one wellintegrity service. Furthermore it is configured to update at least oneof the value and associated uncertainty of at least one of the wellboremodel parameters, thereby updating the wellbore model, based on thesensor data; and determining a state of the well barrier based on theupdated wellbore model.

The one or more computing system processors may include a first subsetof processors situated at the resource site and a second subset ofprocessors situated remotely from the resource site. The second subsetof processors generates the execution plan and the first subset ofprocessors executes the execution plan, and the second subset ofprocessors is configured to transmit the execution plan to the firstsubset of processors. In this case, the first subset of processors maybe configured to transmit at least one of the acquired sensor data andthe at least one of updated wellbore model parameter value andassociated uncertainty to the second subset of processors. The systemmay also include a user terminal situated remotely from the sets (iefirst and second subsets) of server and comprising at least a userinterface, and wherein the one or more computing system processors, inparticular the second subset of processors is configured to transmit thestate of the well barrier to the user terminal for presenting the stateof the well barrier on the user terminal.

The system may also include the downhole tool and the one or morecomputing system processors is configured to communicate with thedownhole tool for sending control commands and receiving data includingthe sensor data. The downhole tool may include an acoustic logging toolconfigured for emitting an acoustic signal in the wellbore and thesensor data may include waveforms received in response to the acousticsignal.

The system may comprise all of the necessary equipments to perform theclaimed method and/or the method disclosed above. In particular, the oneor more computing system processors may be configured to perform anyembodiment of the method disclosed above.

The disclosure also relates to a method for performing at least one wellintegrity service in a wellbore formed in a geological formation at aresource site and having a well barrier including a casing and anannulus formed between the casing and the geological formation. The atleast one well integrity service includes acquiring sensor data relativeto the well barrier with one or more downhole tool situated in thewellbore. The method includes receiving an execution plan at theresource site, the execution plan including a sequence of actions toperform the at least one well integrity service, each action beingassociated with at least one of a failure condition and a successcondition. It further includes executing the execution plan at theresource site, which includes controlling operation of one or moreequipments comprising the one or more downhole tools to acquire thesensor data. Executing the execution plan includes providing datarelative to the well barrier based on acquired sensor data, executing atleast a first action in response to a success condition of the dataprovisoin, the at least first action including controlling the qualityof the data relative to the well barrier, by comparing at least onecharacteristic derived from the data relative to the well barrier to anexpected characteristic. The quality state is determined either asacceptable or undesirable. Executing the execution plan also includesexecuting at least a second action in response to obtaining acceptablequality state, and a third action in response to obtaining anundesirable quality state.

The execution plan may include at least one of a wellbore modelincluding at least one of a value and an uncertainty of one or morepredetermined wellbore parameters, and a parameterization modelincluding at least acquisition parameter values for one or moreequipments including the downhole tool. Executing the execution mayinclude parametrizing the downhole tools as per the parametrizationmodel to provide the data relative to the well barrier.

The execution plan includes updating at least one of the wellbore modeland the parametrization model in response to an undesirable qualitycontrol state.

When providing the data relative to the well barrier includes acquiringsensor data in an at least one first target location, executing theexecution plan may include acquiring sensor data in an at least onesecond target location in response to an undesirable quality controlstate, wherein the at least second target location is the same ordistinct from the at least one first target location.

Controlling the quality of the data relative to the well barrier datauses a machine learning model that determines the quality of the datarelative to the well barrier data based on historical data. The expectedcharacteristic of the acquired sensor data may for instance bedetermined based on historical data.

In an embodiment, the quality control operation is performed based on aQC model included in the execution plan and including the expectedcharacteristic.

In an embodiment, data relative to the well barrier is the sensor dataand providing the data relative to the well barrier includes acquiringthe sensor data using the downhole tool in at least one target location.The quality control operation may then include controlling a quality ofone or more acquisition parameter used during sensor data acquisition,wherein the acquisition parameters include one or more downhole toolparameters, in particular selected from the group consisting ofacquisition time window, a gain, a filter, a voltage, an intensity.

In an embodiment, executing the execution plan includes executing atleast one third action in response to undesirable quality regarding oneof the downhole tool parameters, wherein the third action comprisesupdating said downhole tool parameter.

Executing the execution plan may then include in response to anundesirable quality state, at least one of updating one or more downholetool parameter and of acquiring sensor data in in an at least one secondtarget location in response, wherein the at least second target locationis the same or distinct from the at least one first target location

In an embodiment, the at least one well integrity service includes anacoustic service performed with a downhole tool including at least oneacoustic transducer configured to emit an acoustic signal, wherein thesensor data includes waveforms received in response to the emittedsignal and the quality control relates to waveform integrity orsignal-to-noise ratio.

In another embodiment, providing the data relative to the well barrierincludes interpreting the acquired sensor data to compute at least oneof a value and associated uncertainty of one or more wellbore modelparameters of a wellbore model, wherein the data quality controlincludes controlling the quality of the computed at least one of a valueand associated uncertainty of one or more wellbore model parameters.

In this case, the computed at least one of a value and associateduncertainty of one or more wellbore model parameters being a function ofpredetermined wellbore model parameters, controlling the quality of thecomputed at least one of a value and associated uncertainty of one ormore wellbore model parameters includes determining change points in thecomputed wellbore model parameter values and deriving change points inthe one or more predetermined wellbore model parameter values, whereinthe derived change points for the one or more predetermined wellboremodel parameter values are compared to expected change points. The oneor more predetermined wellbore model parameters may be relative to atleast one of the borehole fluid, the casing internal diameter and thecasing thickness.

In response to obtaining undesirable quality state regarding thecomputed at least one value and associated uncertainty of one or morewellbore model parameters, executing the plan may include at updatingone or more of the predetermined wellbore model parameters.

In response to an acceptable state of the computed at least one valueand associated uncertainty of one or more wellbore model parameters,executing the execution plan may include at least one of transmittingthe computed at least one value and associated uncertainty of one ormore wellbore model parameters to a remote server, updating the wellboremodel and determining a state of the well barrier.

In another embodiment, executing the execution plan includes acquiringthe sensor data using the downhole tool in at least one target location.In response to a success condition of the data acquisition, executingthe execution plan includes controlling the quality of the sensor data,by comparing at least a characteristic derived from the sensor data toan expected characteristic. The quality control outputs an acceptable orundesired state for the sensor data quality. In response to anacceptable state of the sensor data quality, executing the executionplan includes interpreting the acquired sensor data to compute at leastone of a value and associated uncertainty of one or more wellbore modelparameters, and, in response to a success condition of the computation,controlling the quality of the computed at least one of a value andassociated uncertainty of one or more wellbore model parameters, bycomparing at least a characteristic derived from the computed at leastone of a value and associated uncertainty of one or more wellbore modelparameters to an expected characteristic, wherein the quality controloutputs an acceptable or undesired state for the computed parametersquality.

The disclosure also relates to a system for performing at least one wellintegrity service in a wellbore, where the wellbore is formed in ageological formation at a resource site and has a well barrier includinga casing and an annulus formed between the casing and the geologicalformation. The at least one well integrity service includes using onemore equipment to acquiring sensor data associated with the wellbarrier. The one or more equipment may include one or more downholetools situated in the wellbore surface equipment (e.g. surface unit),and combinations thereof. The system includes one or more computingsystem processors configured to receive a model of the wellbore, selectand/or receive at least one well integrity service to perform in awellbore, generate an execution plan, receive an execution plan at theresource site, execute the execution plan at least in part at theresource site, update one or more values and associated uncertaintiesassociated with wellbore model parameters, and determine a state of thewell barrier based upon the updated wellbore model and data captured byone or more equipment. The execution plan may include a sequence ofactions to perform the at least one well integrity service, wherein eachaction is associated with at least one of a failure variable and asuccess variable; and execute the execution plan, which includescontrolling operation of one or more equipment comprising the one ormore downhole tools to perform the sequence of actions of the executionplan. The sequence of actions includes at least: (1) capturing dataassociated with the well barrier, where the data is based on sensordata; (2) at least a first action in response to a success variable ofthe captured or acquired data, where the first action includes a qualitycontrol operation on the captured data, where the quality controloperation compares at least one characteristic associated with thecaptured data to an expected characteristic, where the quality statedata is determined either as an acceptable status or an undesirablestatus; (3) where the quality state data response is an acceptablestatus: at least a second action, and (4) where the quality state dataresponse is an undesirable status: at least a third action.

The system may further include the downhole tool, and the set of one ormore computing system processors may be configured to communicate withthe downhole tool for sending control commands and receiving dataincluding the sensor data. The downhole tool may include an acousticlogging tool configured for emitting an acoustic signal in the wellboreand the sensor data includes waveforms received in response to theacoustic signal.

The one or more computing system processors are configured to executeall of the features claimed in the corresponding method. It may controland communicate to all necessary equipment to execute the actions thatare discussed herein.

The systems and methods described in this disclosure provideimprovements in autonomous operations at resource sites such as oil andgas fields. The systems and methods described allow an orderedcombination of new results in autonomous operations including wellintegrity operations. The systems and methods described cannot beperformed manually in any useful sense. Simplified systems may be usedfor illustrative purposes, but it will be appreciated that thedisclosure extends to complex systems with many constraints therebynecessitating new hardware-based processing system described herein. Theprinciples disclosed may be combined with a computing system to providean integrated and practical application to achieve autonomous operationsin oil and gas fields.

These systems, methods, processing procedures, techniques, and workflowsincrease effectiveness and efficiency. Such systems, methods, processingprocedures, techniques, and workflows may complement or replaceconventional methods for identifying, isolating, transforming, and/orprocessing various aspects of data that is collected from a subsurfaceregion or other multi-dimensional space to enhance flow simulationprediction accuracy.

A benefit of the present disclosure is that more effective methods forwell integrity operations may be employed. While any discussion of orcitation to related art in this disclosure may or may not include someprior art references, Applicant neither concedes nor acquiesces to theposition that any given reference is prior art or analogous prior art.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to explain the principlesof the invention and its practical applications, to thereby enableothers skilled in the art to use the invention and various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed is:
 1. A method for monitoring a well barrier of awellbore formed in a geological formation at a resource site, whereinthe well barrier includes at least a casing and an annulus situatedbetween the casing and the geological formation, the method comprising:receiving, using one or more computing system processors, a model of thewellbore, the wellbore model including a plurality of wellbore modelparameters each having at least one of a wellbore model parameter valueand associated uncertainty; identifying, using the one or more computingsystem processors, at least one well integrity service to perform in thewellbore with a downhole tool, wherein the at least one well integrityservice comprises capturing sensor data associated with the wellbarrier; generating, using the one or more computing system processors,an execution plan, the execution plan including at least one operationcomprised in one or more operations associated with performing the atleast one well integrity service; and executing the execution plan atthe resource site, wherein it includes a control operation of one ormore equipment at the resource site; updating at least one of the valueand associated uncertainty of at least one of the wellbore modelparameters; and determining a state of the well barrier based on theupdated wellbore model.
 2. The method of claim 1 wherein the downholetool includes one or more acoustic logging tools; and acquiring sensordata includes acquiring one or more waveforms obtained in response to anemitted signal.
 3. The method of claim 1, wherein the identifying stepfurther comprises: receiving, using the one or more computing systemprocessors, an objective parameter, wherein the objective parameter is afunction of at least one wellbore model parameter, and wherein theobjective parameter relates to a state of the well barrier; computing,using the one or more computing system processors, an objectiveparameter value and objective parameter uncertainty using the wellboremodel; and identifying at least one well integrity service based on oneor more of the wellbore model and the objective parameter.
 4. The methodof claim 3, wherein the identifying step further comprises: executing,using the one or more computing system processors, one or moresimulations varying at least one of the value and associated uncertaintyof at least one of the wellbore model parameters according to one ormore scenarios; and computing, using the one or more computing systemprocessors, at least one of a forecasted objective parameter value andforecasted objective parameter uncertainty associated with eachscenario; and identifying, using the one or more computing systemprocessors, the at least one well integrity service based on at leastone of the forecasted objective parameter value and forecasteduncertainty associated with each scenario.
 5. The method of claim 3,wherein the objective parameter is an indicator of a casing corrosion,or of a composition of the annulus.
 6. The method of claim 1, whereinthe wellbore model includes at least one of a borehole fluid model, acasing model, and an annulus model.
 7. The method according to claim 1,wherein the at least one well integrity service is an at least one firstwell integrity service, further comprising: identifying, using the oneor more computing system processors, an at least one second wellintegrity service to be performed in the wellbore based on the updatedwellbore model; updating, using the one or more computing systemprocessors, the execution plan to perform the at least one second wellintegrity service; and executing the updated execution plan at theresource site.
 8. The method of claim 1, wherein the control operationincludes acquiring, using the one or more computing system processors,sensor data relative to a state of the well barrier.
 9. The method ofclaim 8, wherein the method further comprises: interpreting the acquiredsensor data; and computing the at least one of value and associateduncertainty of one or more updated wellbore model parameters.
 10. Themethod of claim 8, further comprising: transmitting, using the one ormore computing system processors, the execution plan from a remoteserver to the resource site.
 11. The method of claim 10, furthercomprising: transmitting, using the one or more computing systemprocessors, at least one of the acquired sensor data and updatedwellbore model from the resource site to a remote server.
 12. The methodof claim 11, further comprising: generating, using the one or morecomputing system processors, a visualization representing the state ofthe well barrier on one or more end user terminals located remote fromthe resource site.
 13. The method of claim 8, wherein the step ofexecuting the execution plan includes a quality control operationcomprising: comparing, using the one or more computing systemprocessors, a characteristic associated with the acquired sensor datawith an expected characteristic to generate a quality state, the qualitystate having one of an acceptable status and an undesirable status. 14.The method of claim 13, wherein the quality state operation furthercomprises: updating, using the one or more computing system processors,at least one wellbore model parameter when the quality state has anacceptable status.
 15. The method of claim 13, wherein the comparingstep is based at least on a QC model, wherein the QC model is includedin the execution plan and including at least one of the expectedcharacteristic, the acceptable state and the undesired state.
 16. Themethod of claim 13, wherein the downhole tool includes one or moreacoustic logging tools; the captured sensor data includes acquiring oneor more waveforms obtained in response to an emitted signal; and thequality control operation relates to waveform integrity orsignal-to-noise ratio.
 17. The method of claim 13, wherein in responseto the quality state having an undesirable status, the quality controloperation further comprises executing one or more of the operationsselected from: updating at least one wellbore model parameter; updatinga parametrization of the downhole tool; and updating the sequence ofactions of the execution plan.
 18. The method of claim 1, wherein eachoperation of the execution plan includes one or more actions, whereineach action comprised in the one or more actions is associated with oneor more preconditions, success conditions and failure conditions. 19.The method of claim 1, wherein the execution plan is generated based ona parametrization model, including a parametrization of one or moreequipment at the resource site, including the downhole tool.
 20. Asystem for monitoring a well barrier of a wellbore formed in ageological formation at a resource site, wherein the well barrierincludes at least a casing and an annulus situated between the casingand the geological formation, the system comprising: one or morecomputing system processors; and memory storing instructions that areexecutable by the one or more computing system processors to: receive awellbore model of the wellbore, the wellbore model including a pluralityof wellbore model parameters each having at least one of wellbore modelparameter value and associated uncertainty; generate an execution planincluding one or more actions to perform at least one well integrityservice based on the wellbore model, wherein the at least one wellintegrity service includes acquiring sensor data relative to a state ofthe well barrier with a downhole tool, execute the one or more actionsassociated with the execution plan at least in part at the resourcesite, wherein the one or more actions includes control operations of oneor more equipment at the resource site; update at least one of the valueand associated uncertainty of at least one of the wellbore modelparameters; and determine a state of the well barrier based on theupdated wellbore model.
 21. The system of claim 20, wherein the one ormore system processors comprises one or more first system processorspositioned at the resource site, wherein the one or more first systemprocessors are configured to receive the execution plan; and one or moresecond system processors positioned remotely from the resource site;wherein the one or more second system processors are configured totransmit the execution plan, wherein the one or more second systemprocessors generate the execution plan and the one or more first systemprocessors control one or more equipment at the resource site accordingto the one or more actions.
 22. The system of claim 21, wherein the oneor more first system processors is configured to transmit at least oneof the acquired sensor data and the at least one of updated wellboremodel parameter value and associated uncertainty to the one or moresecond system processors, wherein the system also includes a userterminal situated remotely from the first and second subsets of serverand comprising at least a user interface, and wherein the second subsetof servers is configured to transmit the state of the well barrier istransmitted to the user terminal for presenting the state of the wellbarrier.
 23. The system of claim 20, including the downhole tool,wherein the one or more computing system processors is configured tocommunicate with the downhole tool for sending control commands andreceiving data including the sensor data, wherein the downhole toolincludes an acoustic logging tool configured for emitting an acousticsignal in the wellbore and the sensor data includes waveforms receivedin response to the acoustic signal.